Multi-ship detection and classification with feature enhancement and lightweight fusion

Ship target tracking and detection are essential procedures in the shipping industry that guarantee ship traffic and marine safety. However, issues including complicated background interference, multi-scale object recognition, and inadequate training for small sample recognition are common with classical detection techniques. To address these challenges, this study proposes an enhanced ship multi-target detection model utilizing a modified YOLOv8 algorithm. In addition to integrating the ESSE module and GSConvns technology into the YOLOv8 backbone, the YOLOv8n model acts as the baseline and integrates Wise-IoU technology. This improvement greatly increased multi-scale feature extraction's efficacy while maintaining the fewest possible parameters. With this change, lightweight fusion is accomplished, and the model's capacity to extract semantic characteristics from ship photos is enhanced, particularly when it comes to identifying targets against intricate backdrops. According to testing results on the Dockship, Seaships, and Infrared Offshore Ship datasets, the enhanced algorithm's average detection accuracy is 82.1%, 99.1%, and 91.7%, respectively. This is a considerable improvement over the baseline model. Furthermore, the model's ability to improve the features, lightweighting, and detecting capabilities of different ship kinds has been validated by IoU computation and ablation experimental analysis. These results highlight how the suggested approach could improve ship target detection's automation, dependability, and quality.