CSA: Channel-Wise Similarity Attention for Vehicle State Classification

Developed for specific missions, CNNs have gradually improved the performance of object classification networks by using various architectures. The weight of the convolutional layer is a crucial factor in feature extraction. However, as the number of layers increases, performance degradation can occur due to problems such as the vanishing gradient. To overcome this problem, networks have evolved to continuously incorporate information from previous feature maps using various attention mechanisms. In this study, a Channel-wise Similarity Attention (CSA) method is proposed to measure the similarity of feature maps between channels and enhance positive information by highlighting it. Additionally, a deformable convolutional kernel is embedded to apply a flexible receptive field around the object area in the image, replacing the fixed receptive field of the conventional CNN layer. The network is trained end-to-end to classify the condition of vehicles on the road using collected drone flight images. The proposed model achieves an accuracy of 86.13% and 302 frames per second with a number of parameters of 1,273,504.