Abstract Recent progress on one-stage detectors focuses on improving the quality of bounding boxes, while they pay less attention to the classification head. In this work, we focus on investigating the influence of the classification head. To understand the behavior of the classifier in one-stage detectors, we resort to the methods of the Explainable deep learning area. We visualize its learned representations via activation maps and analyze its robustness to image scene context. Based on the analysis, we observe that the classifier limits the performance of the detector due to its limited receptive field and the lack of object locations. Then, we design a simple but efficient location-aware multi-dilation module (LAMD) to enhance the weak classifier. We conduct extensive experiments on the COCO benchmark to validate the effectiveness of LAMD. The results suggest that our LAMD can achieve consistent improvements and leads to robust detection across various one-stage detectors with different backbones.

中文翻译：

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具有位置感知分类器的鲁棒单阶段目标检测

摘要 一级检测器的最新进展侧重于提高边界框的质量，而较少关注分类头。在这项工作中，我们专注于调查分类头的影响。为了理解一级检测器中分类器的行为，我们求助于可解释深度学习领域的方法。我们通过激活图可视化其学习到的表示，并分析其对图像场景上下文的鲁棒性。基于分析，我们观察到分类器由于其有限的感受野和缺少目标位置而限制了检测器的性能。然后，我们设计了一个简单但有效的位置感知多扩张模块（LAMD）来增强弱分类器。我们对 COCO 基准进行了大量实验，以验证 LAMD 的有效性。