Mask-guided deep learning fishing net detection and recognition based on underwater range gated laser imaging.

• For fishing net detection, range gated laser imaging technology is used to obtain high quality underwater fishing net images with less water backscattering and background noise. • For fishing net recognition, a deep-learning based dual-phase training strategy is proposed to avoid overfitting. • The highest overall accuracy of the proposed method reaches 95.49% in fishing net classification task. In this paper, a mask-guided deep learning fishing net detection and recognition method based on underwater range gated laser imaging is proposed. Range gated laser imaging technology is used to obtain high quality underwater fishing net images with less water backscattering effect and background noise. A dual-phase training strategy including mask-guided feature extraction phase and classification finetune phase is proposed to avoid overfitting of training the neural network. The mask-guided feature extraction phase takes advantages of image segmentation training from synthetic dataset to get a better feature extraction performance. The highest overall accuracy of the proposed method reaches 95.49% in fishing net classification task under finetuned weight configuration. The proposed method can effectively help unmanned underwater vehicles and robots from entangling by fishing nets as well as retrieving derelict fishing nets for marine environment protection. [ABSTRACT FROM AUTHOR]