Aiming at the problem of low visual recognition accuracy of waste materials (copper, brass, aluminum and plastic) in the scrap automobile industry, a novel YOLO V4-tiny algorithm was proposed by adding Mosaic data enhancement and improving the Efficient Channel Attention (ECA) mechanism to the original feature extraction network, which strengthened the learning ability of the target detection algorithm and made the network focus on effective features. Suppress interference features, enhance the model's attention to useful information, improve the detection ability of the algorithm. The improved network model was trained, verified and tested on the self-made data set. The results show that the algorithm can effectively improve the identification accuracy of scrap materials of scrapped vehicles, and finally improve the mAP (mean Average Precision) of the existing YOLOV4-tiny algorithm from 91.33% to 95.17%.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.