Infrared Ship Detection Using Attention Mechanism and Multiscale Fusion

To address the issue of inadequate accuracy and real-time performance of infrared ship target detection methods on coastal defense scenarios, a novel lightweight ship detection algorithm based on improved YOLOv7 framework is proposed. This framework incorporates several enhancements to augment its capabilities. First, to achieve model lightweight processing, the algorithm integrates the MobileNetv3 network into the architecture of the Backbone network. This addition contributes to efficient computation and model size reduction. Second, an attention mechanism is introduced within the Neck network to mitigate noise and interference, thereby improving the network's feature extraction capability. In addition, we employ a bidirectional weighted feature pyramid to enhance feature fusion within the network, promoting more effective information integration. Finally, the algorithm incorporates Wise IoU to optimize the loss function, improving convergence speed and model accuracy. Experimental evaluations on the Arrow dataset demonstrate noteworthy improvements over the standard YOLOv7 approach. Specifically, the proposed enhanced algorithm exhibits a 0. 9 percentage points increase in accuracy, 2. 5 percentage points increase in recall, and 1. 2 percentage points increase in mean average precision (mAP) at IoU thresholds of 0. 5 and 0. 5: 0. 95. In addition, it achieves approximately 38. 4% reduction in model parameters and a 65. 5% reduction in floating point operations per second (FLOPs). This enhanced algorithm delivers superior inspection accuracy while meeting the speed requirements for efficient ship inspection. Consequently, it effectively enables high-speed and high-precision ship detection.