Soldier Detection Based on Deep Learning and Attention Mechanism

In today's complex battlefield environment, traditional combat methods have proven to be inadequate. With the rapid development of computer vision and target detection technologies, the demand for intelligent weapons to automatically identify soldier targets is becoming increasingly prominent. This study aims to explore the soldier target detection problem through deep learning by leveraging an improved YOLOv8 algorithm. Initially, a diverse set of soldier data was collected using methods such as web scraping and movie screenshots, and subjected to preprocessing techniques like data augmentation to construct a dataset covering a comprehensive range of scenarios. Subsequently, the GAM attention mechanism was introduced to further enhance model performance. The experimental results clearly demonstrate that compared to the base model, the proposed optimized model exhibits superior capabilities in multitarget prediction and occlusion detection in soldier target detection tasks, showcasing outstanding detection efficiency and accuracy.