YOLOv7-DBV: A Lightweight Object Detection Algorithm Based on Cross-Stage Local Network

Object detection algorithms that achieve high accuracy in complex application scenarios often result in complex network structures and exhibit poor detection performance for deformed objects and densely distributed small objects. These complex networks have high hardware requirements for mobile terminals or devices that are resource-constrained, significantly limiting their practical applications. To balance detection accuracy and network complexity, we propose a lightweight object detection algorithm based on a cross-stage local network, termed YOLOv7-DBV. First, a lightweight VoV-GSDSBCSP structure is designed to optimize the Head network structure of YOLOv7. Next, DCNv3 is introduced to enhance the YOLOv7 backbone network, improving the extraction of feature information for deformed objects. Finally, a Bi-level Routing Attention (BRA) mechanism is incorporated to further optimize the backbone network of YOLOv7, emphasizing the feature information of small objects. Experiments on a public underwater object detection dataset demonstrate that the YOLOv7-DBV model increases mAP0.5 by 1.05% and reduces the number of parameters by 15.31% compared to the original YOLOv7 model. The proposed algorithm achieves a better balance between detection accuracy and network complexity, outperforming YOLOv7, YOLOv6l, YOLOv5l, and other algorithms.