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In edge detection of images in a convolutional neural network (CNN), each neuron in the first layer corresponds to a small local receptive field in the input image. This receptive field is essentially a small region defined by the dimensions of the convolutional filter or kernel. When a neuron in the first layer processes an input, it does so by applying a convolution operation. This operation involves taking the element-wise product of the filter weights and the corresponding pixel values in the receptive field and then summing up these products. This sum is then passed through an activation function. filters can be designed to respond to specific patterns in the input, such as changes in intensity or color. For example, a simple edge detection filter might have positive weights on one side and negative weights on the other. When this filter is convolved with an image, it responds strongly to areas where there is a sharp transition in intensity, indicating the presence of an edge. The network checks whether nearby pixels have similar colors or not. The convolutional operation, combined with weight multiplication and activation functions, allows the network to learn to detect and respond to different features in the input, including edges. As the network goes through training, the weights in these filters are adjusted to capture the patterns and features relevant to the task at hand.

Figure 3725a illustrate the convolution operation for edge detection.

Figure 3725a. Convolution operation for edge detection (code).

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