Real-time marine target recognition method of underwater inspection robot based on computer vision

In the complex marine environment, target recognition is difficult, and the real-time detection has a slow speed. In this article, a target recognition method combining underwater generative adversarial network and improved YOLOv4 is proposed, which is named M-YOLOv4. Firstly, the images collected by the underwater inspection robot are enhanced using the underwater generative adversarial network algorithm to obtain the training datasets. Secondly, the YOLOv4 target detection algorithm combines the feature extraction network of MoblieNetv3 for lightweight processing, which reduces the network model size, and reduces the number of algorithm calculations and parameters. Then, change the size of the spatial pyramid pooling module pooling kernel, which can enlarge receptive field and integrate characteristics of different receptive fields. Finally, the processed datasets are transferred to the improved M-YOLOv4 algorithm for training, and the trained model is transplanted to the Jetson Nano hardware device for real-time detection. The results of experiments show that the mean average precision value of the improved M-YOLOv4 recognition is 90.77%, which is 2.02% higher than that of the unimproved one. The frame per second value of the lightweight YOLOv4 algorithm with MobileNetv3 is 27, an increase of 12 compared with YOLOv4. The improved M-YOLOv4 algorithm can perform accurate detection of marine multi-targets on embedded devices.