Why Average Pooling is Preferred Over Strided Convolution for Small Object Detection

To avoid suboptimal handling of zero-padding strategies in small object prediction tasks such as traffic sign detection, we opt for an average pooling layer with a 3x3 kernel size instead of a strided convolution in the transition block. Average pooling provides a more efficient and effective approach for these tasks, as it eliminates the need for complex zero-padding strategies that can often hinder performance in the context of small objects.