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Detecting deep-sea megabenthic organisms is of foremost importance for seabed resource surveys, typical habitat protection, and biodiversity surveys. However, the complexity of the deep-sea environment, uneven illumination, and small biological targets that are easily obscured all increase target detection difficulty significantly. To address these, this paper proposes a deep-sea megabenthic detection algorithm, DS-YOLO, based on YOLOv5s. To improve the detection ability of the model for deep-sea megabenthic organisms, the space-to-depth module and the spatial pyramid pooling cross stage partial channel module are introduced in the Backbone layer to enlarge the receptive field and enhance the retention of small-scale features. Then, the space-to-depth and normalization-based attention modules and the Add and Concat functions of the bidirectional feature pyramid network are introduced in the Neck layer to increase the multiscale fusion ability of the model and highlight the insignificant features. Finally, the two branches of the decoupling header output the category and location of the target, which causes the model to utilize the feature information to the maximum extent. Experiments showed that DS-YOLO improved mAP0.5 from 89.6% to 92.4% and mAP0.5:0.95 from 65.7% to 72.3% compared to the original YOLOv5s on the homemade dataset and outperformed other algorithms in the YOLO series. DS-YOLO reaches 84.7 FPS for deployment on mobile platforms. In addition, the combined DS-YOLO and DeepSORT algorithm can be used to calculate the abundance and community structure of deep-sea megabenthos. The model outperforms general target detection models for deep-sea megabenthos detection and is suitable for use in complex deep-sea environments.
Wang et al. (Tue,) studied this question.