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.

翻译：本文提出了一种面向RoboCup标准平台联赛中移动机器人目标检测的创新方法，主要聚焦于足球检测。该挑战在于如何在变化的照明条件和快速运动导致的模糊图像中实现动态目标检测。为应对这一挑战，本文设计了一种专为计算受限机器人平台优化的卷积神经网络架构。所提出的CNN经过训练能够对图像补丁中的单一目标实现高精度分类，并精确定位其空间坐标。文章进一步将早退机制集成到现有高精度CNN架构中，以降低背景类中易拒绝样本的计算成本。训练过程采用基于置信度损失与位置损失的复合损失函数，并引入动态加权与数据增强策略。所提方法在验证集上达到100%的精确率与近87%的召回率，同时保持每假设约170 μs的执行时间。将所提方法与早退机制结合后，相较于原始CNN可实现平均超过28%的运行时优化。总体而言，本文为计算受限机器人平台上的增强目标检测（尤其是足球检测）提供了一种高效解决方案。