A Benchmark Data Set and Evaluation of Deep Learning Architectures for Ball Detection in the RoboCup SPL

This paper presents a benchmark data set for evaluating ball detection algorithms in the RoboCup Soccer Standard Platform League. We cr eated a la- belled data set of images with and without ball derived from vision log files rec- orded by multiple NAO robots in various lighting conditions. The data set con- tains 5209 labelled ball image regions and 10924 non - ball regions . Non - ball im- age region s all contain features that had been classified as a potential ball candi- date by an existing ball detector. The data set was used to train and evaluate 25 2 different Deep Convolutional Neural Network (CNN) architectures for ball de- tection. In order to control computational requirements , this evaluation focused on networks with 2 – 5 layers that could feasibly run in the vision and cognition cycle of a NAO robot using two cameras at full frame rate (2×30 Hz). The results show that the classification perfo rmance of the networks is quite insensitive to the details of the network design including input image size, number of layers and number of outputs at each layer . In an effort to reduce the computational requirements of CNNs we evaluated XNOR - Net architect ure s which quantize the weigh ts and ac tivations of a neural network to binary values . We examined XNOR - Nets corresponding to the real - valued CNNs we had already tested in or- der to quantify the effect on classification metrics. The results indicate that bal l classification performance degrad es by 12% on average when changing from real - valued CNN to corresponding XNOR - Net

Object detection for KRSBI robot soccer using PeleeNet on omnidirectional camera

Kontes Robot Sepak Bola Indonesia (KRSBI) is an annual event for contestants to compete their design and robot engineering in the field of robot soccer. Each contestant tries to win the match by scoring a goal toward the opponent's goal. In order to score a goal, the robot needs to find the ball, locate the goal, then kick the ball toward goal. We employed an omnidirectional vision camera as a visual sensor for a robot to perceive the object’s information. We calibrated streaming images from the camera to remove the mirror distortion. Furthermore, we deployed PeleeNet as our deep learning model for object detection. We fine-tuned PeleeNet on our dataset generated from our image collection. Our experiment result showed PeleeNet had the potential for deep learning mobile platform in KRSBI as the object detection architecture. It had a perfect combination of memory efficiency, speed and accuracy

Real-Time Object Recognition using a Multi-Framed Temporal Approach

Computer Vision involves the extraction of data from images that are analyzed in order to provide information crucial to many modern technologies. Object recognition has proven to be a difficult task and programming reliable object recognition remains elusive. Image processing is computationally intensive and this issue is amplified on mobile platforms with processor restrictions. The real-time constraints demanded by robotic soccer in RoboCup competition serve as an ideal format to test programming that seeks to overcome these challenges. This paper presents a method for ball recognition by analyzing the movement of the ball. Major findings include enhanced ball discrimination by replacing the analysis of static images with absolute change in brightness in conjunction with the classification of apparent motion change

A Benchmark Data Set and Evaluation of Deep Learning Architectures for Ball Detection in the RoboCup SPL

This paper presents a benchmark data set for evaluating ball detection algorithms in the RoboCup Soccer Standard Platform League. We cr eated a la- belled data set of images with and without ball derived from vision log files rec- orded by multiple NAO robots in various lighting conditions. The data set con- tains 5209 labelled ball image regions and 10924 non - ball regions . Non - ball im- age region s all contain features that had been classified as a potential ball candi- date by an existing ball detector. The data set was used to train and evaluate 25 2 different Deep Convolutional Neural Network (CNN) architectures for ball de- tection. In order to control computational requirements , this evaluation focused on networks with 2 – 5 layers that could feasibly run in the vision and cognition cycle of a NAO robot using two cameras at full frame rate (2×30 Hz). The results show that the classification perfo rmance of the networks is quite insensitive to the details of the network design including input image size, number of layers and number of outputs at each layer . In an effort to reduce the computational requirements of CNNs we evaluated XNOR - Net architect ure s which quantize the weigh ts and ac tivations of a neural network to binary values . We examined XNOR - Nets corresponding to the real - valued CNNs we had already tested in or- der to quantify the effect on classification metrics. The results indicate that bal\ud l classification performance degrad es by 12% on average when changing from real - valued CNN to corresponding XNOR - Net

A Benchmark Data Set and Evaluation of Deep Learning Architectures for Ball Detection in the RoboCup SPL

This paper presents a benchmark data set for evaluating ball detection algorithms in the RoboCup Soccer Standard Platform League. We cr eated a la- belled data set of images with and without ball derived from vision log files rec- orded by multiple NAO robots in various lighting conditions. The data set con- tains 5209 labelled ball image regions and 10924 non - ball regions . Non - ball im- age region s all contain features that had been classified as a potential ball candi- date by an existing ball detector. The data set was used to train and evaluate 25 2 different Deep Convolutional Neural Network (CNN) architectures for ball de- tection. In order to control computational requirements , this evaluation focused on networks with 2 – 5 layers that could feasibly run in the vision and cognition cycle of a NAO robot using two cameras at full frame rate (2×30 Hz). The results show that the classification perfo rmance of the networks is quite insensitive to the details of the network design including input image size, number of layers and number of outputs at each layer . In an effort to reduce the computational requirements of CNNs we evaluated XNOR - Net architect ure s which quantize the weigh ts and ac tivations of a neural network to binary values . We examined XNOR - Nets corresponding to the real - valued CNNs we had already tested in or- der to quantify the effect on classification metrics. The results indicate that bal l classification performance degrad es by 12% on average when changing from real - valued CNN to corresponding XNOR - Net

A Benchmark Data Set and Evaluation of Deep Learning Architectures for Ball Detection in the RoboCup SPL

This paper presents a benchmark data set for evaluating ball detection algorithms in the RoboCup Soccer Standard Platform League. We cr eated a la- belled data set of images with and without ball derived from vision log files rec- orded by multiple NAO robots in various lighting conditions. The data set con- tains 5209 labelled ball image regions and 10924 non - ball regions . Non - ball im- age region s all contain features that had been classified as a potential ball candi- date by an existing ball detector. The data set was used to train and evaluate 25 2 different Deep Convolutional Neural Network (CNN) architectures for ball de- tection. In order to control computational requirements , this evaluation focused on networks with 2 – 5 layers that could feasibly run in the vision and cognition cycle of a NAO robot using two cameras at full frame rate (2×30 Hz). The results show that the classification perfo rmance of the networks is quite insensitive to the details of the network design including input image size, number of layers and number of outputs at each layer . In an effort to reduce the computational requirements of CNNs we evaluated XNOR - Net architect ure s which quantize the weigh ts and ac tivations of a neural network to binary values . We examined XNOR - Nets corresponding to the real - valued CNNs we had already tested in or- der to quantify the effect on classification metrics. The results indicate that bal l classification performance degrad es by 12% on average when changing from real - valued CNN to corresponding XNOR - Net