1/17/2024 0 Comments Overloaded truck pictures![]() In our experimental evaluation, our method achieved a recognition accuracy of 85% when the training set consisted of 20 sets of photos, and it reached 100% as the training set gradually increased to 50 sets of samples. Furthermore, our method also demonstrated outstanding performance on a small-scale dataset. The improved MMAL-Net achieved an accuracy of 95.03% on the competitive benchmark dataset, Stanford Cars, demonstrating its superiority over other established methods. The proposed method analyzes the captured images to precisely identify the models of trucks passing through automatic weighing stations on the highway. Vehicle identification involves using frontal and side truck images, while APPM is applied for local segmentation of the side image to recognize individual parts. The method utilizes the improved MMAL-Net for truck model recognition. This paper introduces a novel method for detecting truck overloading. Detecting and preventing truck overloading is of utmost importance for maintaining road conditions and ensuring the safety of both road users and goods transported. However, the overloading of trucks poses serious challenges to road infrastructure and traffic safety. 2College of Information Engineering, Lanzhou University of Finance and Economics, Lanzhou, ChinaĮfficient and reliable transportation of goods through trucks is crucial for road logistics.1Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China.Jiachen Sun 1 Jin Su 2 Zhenhao Yan 1 Zenggui Gao 1 Yanning Sun 1 Lilan Liu 1 *
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