Underwater Objective Detection Algorithm Based on YOLOv8-Improved Multimodality Image Fusion Technology

The field of underwater robotics is experiencing rapid growth, wherein accurate object detection constitutes a fundamental component. Given the prevalence of false alarms and omission errors caused by intricate subaquatic conditions and substantial image noise, this study introduces an enhanced detection framework that combines the YOLOv8 architecture with multimodal visual fusion methodology. To solve the problem of degraded detection performance of the model in complex environments like those with low illumination, features from Visible Light Image are fused with the Thermal Distribution Features exhibited by Infrared Image, thereby yielding more comprehensive image information. Furthermore, to precisely focus on crucial target regions and information, a Multi-Scale Cross-Axis Attention Mechanism (MSCA) is introduced, which significantly enhances Detection Accuracy. Finally, to meet the lightweight requirement of the model, an Efficient Shared Convolution Head (ESC_Head) is designed. The experimental findings reveal that the YOLOv8-FUSED framework attains a mean average precision (mAP) of 82.1%, marking an 8.7% enhancement compared to the baseline YOLOv8 architecture. The proposed approach also exhibits superior detection capabilities relative to existing techniques while simultaneously satisfying the critical requirement for real-time underwater object detection. Moreover, the proposed system successfully meets the essential criteria for real-time detection of underwater objects.