As the core equipment of water safety, life jackets play an indispensable role in the safety field and rescue applications. Although existing deep learning techniques and object detection methods have achieved remarkable success in various fields, there are still many challenges in applying them to life jacket detection. Complex environmental changes, occlusion problems caused by water surface fluctuations, etc. will affect the detection effect. In addition, there are relatively few datasets suitable for life jacket detection, which makes many existing detection models lack stability and generalization ability in practical application scenarios. In order to address these challenges, this paper aims to propose an efficient and accurate detection method for life jacket object detection. Specifically, we first review the relevant work of sea rescue, then collect and label a life jacket-related dataset, and then introduce EVC block based on the Yolov7 network to aggregate the local corner area information of the image, and propose an improved life jacket target detection method E-Yolov7. In the experimental and evaluation section, the proposed method was tested, comparing it with Yolov5s, Yolov7-Tiny, Yolov7x, and Yolov7. Experiments show that the improved model E-Yolov7 has better performance in the life jacket detection task, and compared with Yolov7, the precision is increased by 3.7%, the mAP is increased by 2.5% and the F1 score is increased by 2%.