YOLO-GE: An Attention Fusion Enhanced Underwater Object Detection Algorithm

Abstract

Underwater object detection is a challenging task with profound implications for fields such as aquaculture, marine ecological protection, and maritime rescue operations. The presence of numerous small aquatic organisms in the underwater environment often leads to issues of missed detections and false positives. Additionally, factors such as the water quality result in weak target features, which adversely affect the extraction of target feature information. Furthermore, the lack of illumination underwater causes image blur and low contrast, thereby increasing the difficulty of the detection task. To address these issues, we propose a novel underwater object detection algorithm called YOLO-GE (GCNet-EMA). First, we introduce an image enhancement module to mitigate the impact of underwater image quality issues on the detection task. Second, a high-resolution feature layer is added into the network to improve the problems of missed detections and false positives for small targets. Third, we propose GEBlock, an attention-based fusion module that captures long-range contextual information and suppresses noise from lower-level feature layers. Finally, we combine an adaptive spatial fusion module with the detection head to filter out conflicting feature information from different feature layers. Experiments on the UTDAC2020, DUO and RUOD datasets show that the proposed method achieves an optimal detection accuracy.

Keywords

• object detection
• feature fusion
• yolov8
• attention mechanism