Maritime ship detection algorithm based on improved YOLOv4

Maritime ship detection technology has important value in both the military field and maritime supervision. In terms of traditional detection method of maritime ship with low accuracy under complicated situations, in this paper, we adopt a new detection approach based on the improvement of YOLOv4 in order to realize automatic testing of maritime ship under complex circumstances by deep learning. It aims to adopt lightweight network GhostNet as features to extract the network. Depth-separable convolution will be converted to pointwise convolution first and then transformed into depthwise convolution. The network parameter will be reduced while ensuring the accuracy of testing. The accuracy of testing of maritime ship will be further improved by revising activation function as SMU, combining lose function Alpha-IoU and redesigning lose function CIOU. In order to verify the performance of the algorithm in foggy environment, the interference of foggy weather environment is fully considered when generating the training dataset of maritime ships. During training, Mosaic data enhancements were added to the samples to enhance experimental robustness. The loss function was improved using label smoothing techniques to prevent overfitting. Experimental results showed that when the confidence level is 0.5, compared with the original YOLOv4, the average accuracy of the proposed algorithm reaches 99.97% when the number of parameters is reduced by nearly 84.92%. When the ship target is tiny, the testing result is also highly accurate. Therefore, the method can meet the accuracy requirements of real-time processing of maritime vessel detection.