KEYWORDS: Object detection, Detection and tracking algorithms, Data modeling, Target detection, Systems modeling, Education and training, Image processing, Data conversion, Image segmentation, Feature extraction
This paper presents an object detection method for traffic management based on the YOLOV7 model, using the bdd100k dataset for experimentation. The results show that the proposed method has good detection performance in traffic scenes. The main contribution of this paper is to improve the accuracy and efficiency of object detection in traffic scenes by applying the YOLOV7 model to the field of traffic management. The research results of this paper are of great significance for the optimization and improvement of traffic management systems. Future research can explore YOLOV7's performance on other target categories and consider algorithm optimizations to improve accuracy on new datasets.
This paper presents a plastic cap defect detection model. Plastic caps play a crucial role in industrial production, but they are susceptible to various defects caused by factors such as raw materials and manufacturing processes. Traditional defect detection methods rely on complex feature engineering and classifiers, leading to limited accuracy. To overcome these limitations, this study proposes a defect detection solution that leverages the YOLO model's renowned fast and end-to-end detection capability. By training on a substantial datasets of labelled plastic cap images, an efficient and accurate defect detection model is constructed. Specifically optimized for plastic cap defects, the model achieves a remarkable accuracy of 96% with low false positive and false negative rates. Comparative experiments and evaluations validate the superior efficiency and accuracy of the proposed method compared to traditional approaches. Consequently, this study presents a highly effective solution for plastic cap defect detection.
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.