Efficient Single Object Detection on Image Patches with Early Exit
  Enhanced High-Precision CNNs
Arne Moos
This paper proposes a novel approach for detecting objects using mobile
robots in the context of the RoboCup Standard Platform League, with a primary
focus on detecting the ball. The challenge lies in detecting a dynamic object
in varying lighting conditions and blurred images caused by fast movements. To
address this challenge, the paper presents a convolutional neural network
architecture designed specifically for computationally constrained robotic
platforms. The proposed CNN is trained to achieve high precision classification
of single objects in image patches and to determine their precise spatial
positions. The paper further integrates Early Exits into the existing
high-precision CNN architecture to reduce the computational cost of easily
rejectable cases in the background class. The training process involves a
composite loss function based on confidence and positional losses with dynamic
weighting and data augmentation. The proposed approach achieves a precision of
100% on the validation dataset and a recall of almost 87%, while maintaining an
execution time of around 170 $\mu$s per hypotheses. By combining the proposed
approach with an Early Exit, a runtime optimization of more than 28%, on
average, can be achieved compared to the original CNN. Overall, this paper
provides an efficient solution for an enhanced detection of objects, especially
the ball, in computationally constrained robotic platforms.