Lightweight Underwater Target Detection Algorithm Driven by Dual-stream Attention Mechanism and Multi-scale Feature Representation

Abstract:

In complex natural environments, enhancing the detection efficiency of underwater biological resources is vital for China??s marine economic development. To address the issues of limited computational resources and poor detection due to underwater complexity, CBM-YOLO, an improved YOLO v8s-based underwater target detection algorithm was proposed. Firstly, CSP-DLN, a lightweight feature extraction module, was designed, combining the advantages of 3 × 3 and 1 × 1 convolutional kernels to efficiently capture detailed spatial features and reduce redundant calculations. Secondly, to address the loss of feature information in underwater biological targets, a new feature fusion network, BGL-FPN, was introduced, which effectively improved detection accuracy through cross-scale connections combined with global and local spatial attention mechanisms. Lastly, max pooling downsampling ( MPD) was proposed, leveraging parallel processing of max pooling and convolutional branches to better capture edges and details of small targets, thereby enhancing their detection capability. Experimental results indicated that the algorithm attained mAP @ 0. 5 of 78. 2% and 69. 1% on the URPC2020 and UDD datasets, respectively, with improvements of 1. 5 and 2. 6 percentage points over the baseline model, while reducing parameters and computations by 47. 3% and 25. 5% . It outperformed other mainstream target detection algorithms. Deployed on the Jetson TX2 embedded device and accelerated by TensorRT, the model achieved mAP@ 0. 5 of 77. 4% and a detection speed of 37. 6 f/ s, enabling real-time underwater detection with high accuracy.