Lifejacket wearing detection for shipboard personnel based on improved YOLOv7

Many of the major casualties caused by ship sailing accidents have proved that life jackets play a vital role in safeguarding the lives of people on board. In this paper, the K-means++ algorithm is used to optimize the target prior frame, and the CBAM attention mechanism is added to the YOLOv7 network to enhance the effective features and weaken the ineffective features in the feature extraction process, to enhance the localization accuracy and detection ability. The BiFPN network is used to enhance the information acquisition degree of the target, and finally, a comparison experiment is conducted with the YOLOv7 model before improvement. The experimental results show that the accuracy of the YOLOv7-CB model after training on the lifejacket dataset reaches 92.3%, the detection rate is 6.4% higher than that of the pre-improvement period, and the detection rate is 8.5% higher, which is good for the lifejacket wearing detection of shipboard personnel in different environments.