Abstract
Underwater object detection presents both significant challenges and opportunities within ocean exploration and conservation. Although the current popular object detection algorithms generally achieve strong performance. Because underwater images are affected by insufficient illumination, wavelength-dependent scattering, and absorption, the detection performance for underwater objects is suboptimal. Therefore, a local channel information encoding method named Partial Semantic Encoding Module (PSEM) and an attention based detection head called Split Dimension Weighting Head (SDWH) are proposed by this paper to enhance the ability of models to extract and integrate semantic features of underwater targets, as well as the capability to locate foreground underwater targets. Specifically, PSEM enhances the fusion of features across multi-scales of the network. It successively completes semantically encoding feature information, followed by residual point-wise addition, and encoding local channel information. SDWH serially weights spatial and channel semantic information of fused features, enhancing the semantic perception of the detectors and the localization ability for foreground underwater objects. PSEM and SDWH are improvements to the neck and detection head of the YOLO series algorithms, respectively. Extensive experiments are conducted on UTDAC2020 and RUOD datasets. On the UTDAC2020 dataset, YOLOv8n improved with PSEM and SDWH achieves a 2.8% mAP increase compared to the original version, YOLOv5n shows a 1% mAP improvement, and YOLOv6n achieves a 3.0% mAP increase. Testing on the RUOD dataset, PSEM and SDWH enable YOLOv8n to achieve a 2.7% mAP improvement. YOLOv5n and YOLOv6n achieve improvements of 1.5% mAP and 3.7% mAP, respectively. Moreover, compared to other real-time underwater SOTA algorithms, YOLOv8n enhanced with PSEM and SDWH achieves the highest mAP of 82.9% on the UTDAC2020 dataset and 80.9% on the RUOD dataset. The proposed PSEM and SDWH are demonstrated to significantly improve the underwater object detection accuracy of YOLO series detectors with acceptable computational cost, and the real-time performance can fully satisfy practical requirements.