Enhanced YOLOv7 Based on Channel Attention Mechanism for Nearshore Ship Detection
Qingyun Zhu, Zhen Zhang, Ruizhe Mu · Electronics · 2025
Nearshore ship detection is an important task in marine monitoring, playing a significant role in navigation safety and controlling illegal smuggling. The continuous research and development of Synthetic Aperture Radar (SAR) technology is not only of great importance in military and maritime security fields but also has great potential in civilian fields, such as disaster emergency response, marine resource monitoring, and environmental protection. Due to the limited sample size of nearshore ship datasets, it is difficult to meet the demand for the large quantity of training data required by existing deep learning algorithms, which limits the recognition accuracy. At the same time, artificial environmental features such as buildings can cause significant interference to SAR imaging, making it more difficult to distinguish ships from the background. Ship target images are greatly affected by speckle noise, posing additional challenges to data-driven recognition methods. Therefore, we utilized a Concurrent Single-Image GAN (ConSinGAN) to generate high-quality synthetic samples for re-labeling and fused them with the dataset extracted from the SAR-Ship dataset for nearshore image extraction and dataset division. Experimental analysis showed that the ship recognition model trained with augmented images had an accuracy increase of 4.66%, a recall rate increase of 3.68%, and an average precision (AP) with Intersection over Union (IoU) at 0.5 increased by 3.24%. Subsequently, an enhanced YOLOv7 algorithm (YOLOv7 + ESE) incorporating channel-wise information fusion was developed based on the YOLOv7 architecture integrated with the Squeeze-and-Excitation (SE) channel attention mechanism. Through comparative experiments, the analytical results demonstrated that the proposed algorithm achieved performance improvements of 0.36% in precision, 0.52% in recall, and 0.65% in average precision ([email protected]) compared to the baseline model. This optimized architecture enables accurate detection of nearshore ship targets in SAR imagery.