Moving from localization to detection, we can proceed in two steps, as shown in the following screenshot: first use small tightly cropped images to train a convolution neural net for image classification, and then use sliding windows of different window sizes (smaller to larger) to classify a test image within that window using the convnet learnt and run the windows sequentially through the entire image, but it’s infeasibly slow computationally.
However, as shown in the next figure, the convolutional implementation of the sliding windows by replacing the fully connected layers with 1 × 1 filters makes it possible to simultaneously classify the image-subset inside all possible sliding windows parallelly, ...
