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xYOLO: A Model For Real-Time Object DetectionIn Humanoid Soccer On Low-End Hardware
Daniel Barry ∗, Munir Shah ¶, Merel Keijsers †,Humayun Khan § and Banon Hopman ‡
Departments: ∗ Computer Science † § HIT Lab NZ ‡ School of Product DesignEmails: ∗ [email protected] ¶ [email protected]
† [email protected] ‡ [email protected] § [email protected]
University of CanterburyChristchurch, New Zealand
Abstract—With the emergence of onboard vision processingfor areas such as the internet of things (IoT), edge computingand autonomous robots, there is increasing demand for compu-tationally efficient convolutional neural network (CNN) modelsto perform real-time object detection on resource constraintshardware devices. Tiny-YOLO is generally considered as oneof the faster object detectors for low-end devices and is thebasis for our work. Our experiments on this network haveshown that Tiny-YOLO can achieve 0.14 frames per second(FPS) on the Raspberry Pi 3 B, which is too slow for soccerplaying autonomous humanoid robots detecting goal and ballobjects. In this paper we propose an adaptation to the YOLOCNN model named xYOLO, that can achieve object detectionat a speed of 9.66 FPS on the Raspberry Pi 3 B. This isachieved by trading an acceptable amount of accuracy, makingthe network approximately 70 times faster than Tiny-YOLO.Greater inference speed-ups were also achieved on a desktopCPU and GPU. Additionally we contribute an annotated Darknetdataset for goal and ball detection.
Index Terms—CNN, RoboCup, Object Detection
I. INTRODUCTION
The popularization of deep learning has done much to fur-ther advancements in computer vision, where modest amountsof computational power allow for the processing of imagesto gain insight on their contents [11]. As real-world imagedata typically has high spatial correlation, convolutional neuralnetworks (CNNs) have been particularly successful in theapplication of object detection in images [26]. Compared tofully connected networks, CNNs offered large computationand size reduction [6].
In this paper our chosen problem domain is RoboCup,an annually held international humanoid robotics competitionwith the goal of producing a team of robots that beat thebest human football players in the world by 2050 [9]. Thereason for this ambitious goal is to help motivate several keyareas in artificial intelligence in a format that the generalpublic understand and appreciate without having insight tothe complexity of the robots themselves. There are many
This project would like to thank the following departments for funding:Computer Science, HIT Lab NZ and School of Product Design.
limitations in the competition in aid of reaching the 2050 goal,such as: robots must be fully autonomous, must be human like(e.g. passive sensors only) and must adhere to an adaptationof the official FIFA rules 1.
Our team, Electric Sheep, competed in the 2019 worldcup with our unique low-cost open-source humanoid roboticsplatform 2. The purpose of designing and building a low-costplatform was to lower the boundary to entry, as larger robotshave seen larger costs in recent years which could discouragenew teams from entering the competition. Our platform, BlackSheep, performs all processing on a Raspberry Pi 3 B includingour vision processing pipeline. As our agent behaviour is verysimple it only requires the detection of goals and balls.
In this paper, we propose a YOLO based CNN model whichcan detect balls and goal posts at ≈10 FPS, which given thecurrent relatively slow speed of robot play, is an acceptableframe rate. Our proposed model is called xYOLO and exploitsthe domain specific attributes such as the requirement of twoclasses (ball and goal post), simple shape features of theobjects and clearly differentiable objects from the background.This allows our model to achieve real-time object detectionspeed with reasonable accuracy.
In Section II we give a brief overview of efforts prior tothe implementation of the neural networks in this domain,why these approaches are not appropriate for the updatedenvironment and the current approaches for object detection.In Section III we describe our network architecture for xYOLOand how it differs from similar existing work. For Section IVwe describe experiments and analyse results for comparablenetworks. Finally, in Section V we evaluate our work anddiscuss future work.
II. RELATED WORK
Traditionally, in the context of the RoboCup humanoidrobotics competition, colour segmentation based techniqueshave been used to detect features of the soccer field, such
1Humanoid rules: https://www.robocuphumanoid.org/materials/rules/2Humanoid platform: https://github.com/electric-sheep-uc/black-sheep
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as goals and balls [16] [17]. These techniques are fast andcan achieve good accuracy in simplistic environments, forexample the use of an orange ball, controlled indoor lightingand yellow coloured goals. However, in light of RoboCup’s2050 goal, teams have seen the introduction of natural light-ing conditions (exposure to sunlight), white goal posts withwhite backgrounds and FIFA balls with a variety of colours.Colour segmentation based techniques fail to perform in thesechallenging scenarios and has mostly pushed the competitiontowards implementing a variety of neural network approaches[12] [25].
CNN based models have shown great progress in termsof object detection accuracy in complex scenarios [21] [15],[10] [24] [20] [1]. However, these high performing computervision systems based on CNNs, although much leaner thanfully connected networks, are still both considerably memoryand computationally exhausting, and achieve real-time perfor-mance only on high-end GPU devices. For this reason, most ofthese models are not suitable for low-end devices such as smartphones or mobile robots. This limits their use in real-timeapplications such as autonomous humanoid robots playingsoccer, as there are power and weight considerations. Thus, thedevelopment of lightweight, computationally efficient modelsthat allow CNNs to work using less memory and on minimalcomputational resources is an active research area [3] [1] [5][23].
Recently, a large number of research papers have beenpublished on the topic of lightweight deep learning modelsfor object detection that are suitable for low-end hardwaredevices [3] [22] [2] [13] [18] [21] [5] [23]. Most of thesemodels are based on SSD [15], SqueezeNet [7], AlexNet [10],and GoogLeNet [24]. Generally, in these models the objectdetection pipeline contains several components such as pre-processing, large numbers of convolution layers, and post-processing. Classifiers are evaluated at various locations inimages and at multiple scales using a sliding window approachor region proposal methods. These complex object detectionpipelines are computationally intensive and consequently slow.XNOR-Networks [18] approximate convolutions using binaryoperation, which is computationally efficient compared to thefloats used in traditional convolutions. An obvious downsideof XNOR networks is the reduction in accuracy for similarlysized networks.
On the other-hand, in you only look once (YOLO), objectdetection is framed as a single regression problem. YOLOworks at the bounding box level rather than pixel level, i.e.YOLO simultaneously predicts bounding boxes and associatedclass probabilities from the entire image in one “look”. One ofthe key advantages of YOLO is its ability to encode contextualinformation, and as a result it makes less mistakes in confusingbackground patches in an image for objects [20] [21].
The “lighter” version of YOLO v3 [21], called Tiny-YOLO,was designed with speed in mind and is generally reported asone of the better performing models in-terms of speed andaccuracy trade-off [21]. Tiny-YOLO has nine convolutionallayers and two fully connected layers. Our experiments suggest
that Tiny-YOLO is able to achieve 0.14 FPS on Raspberry Pi3, which is far from real-time object detection.
From the results reported in [21], it can be concludedthat these object detectors are not able to give real-timeperformance on low-end hardware with minimal computingresources (e.g. humanoid robots with a Raspberry Pi as thecomputing resource). In our robots, we are using one computeresource for several different processes, such as the walkengine, self-localization, etc. The vision system is left withapproximately a single core to perform all object detection.
III. NETWORK
Our proposed network, xYOLO, is derived from YOLO v3tiny [21], specifically we use AlexeyAB’s Darknet fork thatallows for XNOR layers and building on the Raspberry Pi 3. Asseen in Figure 1, xYOLO utilizes both normal convolutionaland XNOR layers in both training and recall. The network hasseveral key changes:
• Reduction in input layer size: Scaling the input image to256 x 256 pixels was the smallest input we could createwithout sacrificing the network’s ability to see detailsat far-distance in the 640 x 480 original image. Due tolimitations of the framework implementation, preservingaspect ratio was not easily possible. Switching from RGBto grey scale input (three channels down to one) hadvery little impact on speed, but largely affected detectionquality, hence we use full colour information. Throughexperimentation we realized that ball detection relies onits context, in this case the green field background.
• Heavily reduced number of filters: Generally, the objectswe are attempting to detect are quite simple in shape andfeatures, meaning this domain specific reduction can bemade. We were able to heavily reduce the size of thenetwork with this change and increase detection speeddramatically.
• Layers m, n, o and p use XNOR: Through experimenta-tion we found that this part of the network was able toswitch to XNOR without affecting training or prediction.Whilst the network size remains the same, not usingfloating point arithmetic gave a marginal speed increaseduring detection. When using XNOR throughout thenetwork (specifically the convolutional layers between aand k) we found the network was unable to detect objectsto any accuracy (see Figure 2). We believe the earlyfeature formation in the network to be highly importantin training and object detection for small networks.
Each year of the RoboCup competition introduces newchallenges, where models have to be retrained using imagescollected and labelled during the setup time of the competition.Consequently, our approach towards designing this networkwas to reduce the training time to below 45 minutes, allowingfor relatively rapid testing of different network configurationsand new soccer field conditions. Figure 2 is an example of anetwork where the parameters are reduced too far, such that
3Darknet fork: https://github.com/AlexeyAB/darknet
abc d
e fg h i j k l m n o p q r s t u v w x
Fig. 1: The network structure is as follows: convolutional: a, c, e, g, i, k, s, v, w, max pool: b, d, f, h, j, l,convolutional XNOR: m, n, o, p, yolo: q, x, upsample: t, route: r, u.
Fig. 2: Example of a network that is too small to detect objects.
it is incapable of detecting objects. In Figure 3 this wouldmanifest itself as the loss mean square error not reducingbelow 6 before 1,000 iterations or models that are not ableto reduce their loss to an acceptable value, i.e. below 1.5.Generally we are able to conclude whether a network has areasonable chance of success within the first 15 minutes oftraining.
IV. EXPERIMENTS AND RESULTS
Experiments are conducted using Darknet [19], an opensource neural network framework. Darknet is fast (written inC language with many optimizations), easy to compile on theRaspberry Pi and supports both CPU and GPU training anddetection. Our proposed model (xYOLO) is compared againstTiny-YOLO (v3) [21] and Tiny-YOLO-XNOR (v3) [18]. Tiny-YOLO is reported as a CNN model with good trade-off be-tween computational efficiency and object detection accuracy[13] [8]. Tiny-YOLO-XNOR is a lightweight implementationof Tiny-YOLO with XNOR [18] in the Darknet framework.Each of the models is adapted for the use of two classes byadjusting the number of filters before the YOLO layers to thefollowing (we have two classes, ball and goal):
filters = (classes+ 5)× 3 (1)
ID Filters Size Input Outputa 2 3 x 3 / 1 256 x 256 x 3 256 x 256 x 2b 2 x 2 / 2 256 x 256 x 2 128 x 128 x 2c 4 3 x 3 / 1 128 x 128 x 2 128 x 128 x 4d 2 x 2 / 2 128 x 128 x 4 64 x 64 x 4e 8 3 x 3 / 1 64 x 64 x 4 64 x 64 x 8f 2 x 2 / 2 64 x 64 x 8 32 x 32 x 8g 16 3 x 3 / 1 32 x 32 x 8 32 x 32 x 16h 2 x 2 / 2 32 x 32 x 16 16 x 16 x 16i 32 3 x 3 / 1 16 x 16 x 16 16 x 16 x 32j 2 x 2 / 2 16 x 16 x 32 8 x 8 x 32k 64 3 x 3 / 1 8 x 8 x 32 8 x 8 x 64l 2 x 2 / 1 8 x 8 x 64 8 x 8 x 64m 128 3 x 3 / 1 8 x 8 x 64 8 x 8 x 128n 32 1 x 1 / 1 8 x 8 x 128 8 x 8 x 32o 64 3 x 3 / 1 8 x 8 x 32 8 x 8 x 64p 21 1 x 1 / 1 8 x 8 x 64 8 x 8 x 21s 16 1 x 1 / 1 8 x 8 x 32 8 x 8 x 16t 2x 8 x 8 x 16 16 x 16 x 16v 32 3 x 3 / 1 16 x 16 x 48 16 x 16 x 32w 21 1 x 1 / 1 16 x 16 x 32 16 x 16 x 21
TABLE I: The following is the network structure of xYOLO.
The models are evaluated on our dataset. The details ofthe dataset are described in Section IV-A. All models weretrained using 90% of images in the dataset and 10% of theseimages were used during testing. Object detection accuracyis measured by mean Average Precision (mAP) and F-Score[14]. All of these models are evaluated using the defaultparameters settings. Computational efficiency of the models ismeasured by inference time and train time (minutes). Modelsare evaluated on the Raspberry Pi 3 B, a standard desktop CPU(Intel i7-6700HQ) and a standard GPU (Nvidea GTX 960M)environment to measure inference time. Since memory is alsoan issue on low-end hardware, we also compared modelsusing network size MB (Mega Bytes) and computational sizeBFLOPs (Billion FLOPs) performance metrics [21].
A. Dataset
One of the contributions of this study is our annotateddataset from the RoboCup 2019 competition using camerasmounted on the robots in both the controlled and naturallighting scenarios. We also used some images from previous
competitions via the Image Tagger community-driven project[4]. Each of these raw images are manually annotated. Thereare two classes in the dataset: ball and goal post. Traditionallypeople used complete goals as a single object. The inside of thegoal is hollow and usually only part of the goal on the field isin the camera frame, making the detection of a full goal oftendifficult. In RoboCup, generally the ball stays on the ground,thus the robot rarely needs to look upwards and detecting onlythe bottom of the goal posts is ideal. In consideration of this,we used bottom of the goal posts to detect goal. In this datasetboth left and right goal posts are considered as two instancesof the same class (goal post).
This dataset contains range of challenging scenarios, suchas natural lighting (sun light spots on the field), shadows, andblurred images since robots are moving. Some of the glimpsesof the complexities of the dataset can be seen in Figure 5. Wehave open sourced this dataset and is available for public use4.
B. Comparative Computational Speed
The key focus of this paper is to achieve real-time objectdetection and localization performance on low-end computinghardware such as Raspberry Pi 3 B. To time models trainingduration, we used a cloud instance with an Nvidea K80 GPUand 55GB RAM. All models were trained for 6000 iterationsand tested for inference speed in both a standard desktopenvironment and a Raspberry Pi. As shown in Table II, traintimes are reported on the cloud instance GPU and inferencespeed is reported on multiple hardware targets.
As shown in the Table II, xYOLO achieved superior per-formance in terms of computational efficiency compared tothe other tested models. For train time xYOLO is ≈5 timesfaster than the other two models. For inference speed, xYOLOachieved 706.36 FPS on the GPU, which is ≈7 times fasterthan Tiny-YOLO-XNOR and ≈9 times faster than Tiny-YOLO. On desktop CPU, xYOLO performed 87 and 35 timesbetter than Tiny-YOLO and Tiny-YOLO-XNOR respectively.On the Raspberry Pi, xYOLO performed 69 times faster thanTiny-YOLO and 25 times faster than Tiny-YOLO-XNOR. Theimproved speed gain is due to small input and filters size.Tiny-YOLO-XNOR averaged 2.52 FPS on the Raspberry Piand Tiny-YOLO at 1 FPS. Both of these models are too slowto be used effectively in the RoboCup competition, as gamesdevelop quickly. On the other hand, xYOLO is capable of ≈10fps on the Raspberry Pi, which is reasonable object detectionspeed, especially for the purpose of humanoid league soccermatches.
In terms of network size, xYOLO is 45 times smaller thanTiny-YOLO and ≈15 times smaller than Tiny-YOLO-XNORin network size. Similarly, xYOLO requires 0.039 BFLOPs,which is significantly lower than other two models. In short,xYOLO outperformed other models on all computationalefficiency metrics.
4Dataset released under a Creative Commons license (free login requiredfor access): https://imagetagger.bit-bots.de/images/imageset/689/
Network Tiny-YOLO Tiny-YOLO-XNOR xYOLOTrain Time (minutes) 183 174 39Inference (FPS) 0.89 2.52 70.69i7-6700HQ stdev 0.059 0.026 0.0018Inference (FPS) 79.97 97.28 706.36GTX 960M stdev 0.00064 0.00055 0.00023Inference (FPS) 0.14 0.39 9.66rPi 3 B stdev 0.064 0.012 0.0012Size (MB) 34.7 12.46 0.82BFLOPs 5.449 5.449 0.039
TABLE II: Computational efficiency and network size results.Note that the reported BFLOPs for Tiny-YOLO-XNOR andxYOLO incorrectly calculate the XNOR layers as FLOPs.
0 1000 2000 3000 4000 5000 6000Iteration Number
0
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2
3
4
5
6
Loss
(mea
n sq
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Tiny-YOLOTiny-YOLO-XNORxYOLO
Fig. 3: Train loss results for the models. It is observed thatthe models took ≈4000 iterations to reach their lowest loss.
C. Comparative Accuracy
The object detection and localization accuracy of the modelsis measured by train loss (mean square error), validation mAP(mean Average Precision on validation dataset), inferencemAP on test dataset and F-score. We used Darknet transferlearning where pre-trained weights (darknet53.conv.74)for the Imagenet dataset are used as initial weights for train-ing. This transfer learning helps models take less than 1000iterations to reduce their loss to less than 6. Figure 3 showstrain loss for the models. Results from the Figure 3 suggeststhat the models took ≈4000 iterations to stabilized and afterthat loss was not greatly reduced. Tiny-YOLO was able reduceits loss to ≈0.5, with xYOLO to ≈1 and Tiny-YOLO-XNORto ≈1.5. Accuracy results (mAP) on the validation set are
Algorithm mAP (Train) mAP (Test) F-scoreTiny-YOLO 94.65 93.69 92Tiny-YOLO-XNOR 68.42 64.75 62xYOLO 68.22 66.75 68
TABLE III: Object detection accuracy results on our humanoidsoccer dataset (see Section IV-A).
0 1000 2000 3000 4000 5000 6000Iteration number
0%
20%
40%
60%
80%
100%Ac
cura
cy (%
)
Tiny-YOLOTiny-YOLO-XNORxYOLO
Fig. 4: Accuracy (mAP) results for the models during trainingon the validation set. It is observed that Tiny-YOLO performedsignificantly better than the other two models.
presented in Figure 4 and Table III. It is observed that modelshave achieved similar accuracy on both train and unseen testsets. Tiny-YOLO achieved significantly better object detectionaccuracy compared to other models. xYOLO was able toachieve ≈68% accuracy on validation dataset, and ≈67% onthe test set, which is good when the speed and size of xYOLOis considered.
D. Qualitative Evaluation
Figure 5 demonstrates object detection results by each of themodels on a challenging scenarios. All models were able todetect both ball and goal post in easy scenarios, where objectsare quite clearly seen. It is observed that the Tiny-YOLOstruggled to detect one goal post in the blurred image scenarios(Figure 5b). In a scenario where there are shadows on the field(Figure 5c), Tiny-YOLO-XNOR was not able to detect bothgoal post or ball, whereas xYOLO was able to detect the ballbut not the goal post and Tiny-YOLO was able to detect bothobjects. In the natural lighting scenario with strong sunlightspots on the field, all models performed well with Tiny-YOLOable to detect partially observable ball. In summary, bothTiny-YOLO and xYOLO have shown advantages in differentscenarios.
V. DISCUSSION
Although xYOLO has less accuracy, it is the only modeltested that was able to achieve ≈10 FPS with an acceptable≈70% accuracy, making it suitable for low-end hardware real-time detection on the Raspberry Pi. For humanoid soccer,robots have to make quick decisions (e.g. to detect a rollingball). For this reason, fast models with slightly lower accuracywork better than highly accurate but slower models. xYOLOprovides a good speed and accuracy compromise for humanoidsoccer, which was achieved by reducing the training time,thereby reducing experiment time and allowed for us to finetune the network to detect objects within the domain.
In future work we look towards performing pre-processingtechniques on the input image to further reduce the size of thenetwork. Additionally we want to leverage the high correlationof inter-frame data through the use of optical flow.
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(a) Easy detection scenario, ball and goal posts are clear.
(b) As robot’s camera is often moving and as a result models have to perform in blurred image scenario.
(c) Object detection results in natural lighting scenario with shadows on the field.
(d) Object detection results in natural lighting scenarios with strong sunlight spots on the field.
Fig. 5: Example object detection results produced by the models. Left side: Tiny-YOLO [21], center: xYOLO, right side: Tiny-YOLO-XNOR. Balls and goals are labelled when each network identifies an object that reaches the detection threshold.It can be observed that xYOLO has better object detection results than Tiny-YOLO-XNOR [18] and comparable result toTiny-YOLO.
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