Since the existing capsule endoscopy detection model has the problem of single lesion detection and low efficiency, a novel lesion region detection method of capsule endoscopic based on the improved YOLOv5 was proposed. The original YOLOv5 had been improved in the following three aspects. Firstly, the convolutional block attention module (CBAM) was incorporated into the Backbone of YOLOv5, which was used to enhance the presentation ability of the target feature. Secondly, a detection head was added to the Head of YOLOv5, which was used to enhance the detection ability of the small targets. Finally, the GIOU loss function of YOLOv5 was replaced with the CIOU loss function, which leaded to quicker convergence during training. The proposed method was tested on the capsule endoscopic image data provided by the First Clinical Medical College of Yangtze University. It's accuracy rate reached 93.6%, the recall rate reached 94.3%, the detection rate reached 97.2% on mAP@0.5 and a detection speed of 0.027 2 s per frame for lesion area detection on the dataset. The experimental results show that the proposed method is effective, flexible, and robust, which can meet the practical needs of clinical medical diagnosis.