Yun Cong Lu
ABSTRACT
Abstract: With the number of passengers increasing year by year, the pressure of security check in railway stations, airports and other transportation places is increasing. Long hours of high-intensity work can easily lead to fatigue of security inspectors, which makes it difficult for them to stay focused in front of the X-ray detection machine. This situation can easily lead to missed detection of controlled knives, and ultimately increase the safety risk of passengers during the journey. In order to solve the above problems, based on the YoloV5 model and the Convolutional Block Attention Module, this paper builds a detection model that can automatically identify the controlled knife in the X-ray image, which is used to help the security inspector to automatically detect the X-ray image in an efficient and fast way. The results show that the final mAP of X-ray image controlled knife detection model based on improved YoloV5 is 0.9169.
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Index Terms
- Controlled knife X-ray image detection model based on improved YoloV5
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