Bi2F-YOLO: a novel framework for underwater object detection based on YOLOv7

Abstract Underwater object detection faces significant challenges, including ambiguity and occlusion, which greatly undermine the accuracy of traditional algorithms. To address these issues, we propose Bi2F-YOLO, an algorithm, specifically designed for underwater environments. Bi2F-YOLO integrates the BiFormer module into the YOLOv7 backbone, utilizing Bi-Level routing attention (BRA) to focus on key features such as object edges and textures. This effectively addresses the problem of object ambiguity. In the detection head, we replace the conventional ELAN component with the FasterNet module. This update enhances detection efficiency and accuracy through the use of partial convolution (PConv), which redistributes the convolution kernel weights based on the sparsity of the input feature map. By doing so, it prevents the dilution of critical underwater object features caused by interference from irrelevant data. This effectively resolves the occlusion problem in underwater target detection while simultaneously reducing model parameters and computational costs. The experimental results show that Bi2F-YOLO achieves 87.3% $${{mAP}}_{50}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mi>mAP</mml:mi> </mml:mrow> <mml:mn>50</mml:mn> </mml:msub> </mml:math> and 86.8% $${{AP}}_{50}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mi>AP</mml:mi> </mml:mrow> <mml:mn>50</mml:mn> </mml:msub> </mml:math> on the RUOD dataset. Compared to existing methods, it reduces model parameters by 14.3% fewer parameters ( Params ) and lowers computational costs by 5.5%, highlighting its efficiency and accuracy in underwater object detection.