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Automated detection and classification of spilled loads on freeways based on improved YOLO network

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Abstract

This study aims to utilize a modified you only look once (YOLO) network to address the detection and classification of spilled loads on freeways. YOLO architecture was augmented in two ways. Firstly, a kernel size of 1 × 1 for the conv layers was used. Secondly, the use of connections between the convolution layers was proposed. For training the network, a synthetic dataset was constructed where ImageNet was used to choose ten types of spilled load objects and KITTI dataset as the background. The objects were blended in the KITTI images' road region, where the road area is segmented through an already trained network previously available. The testing dataset was constructed with manually taken photographs. Experiment results showed that the training model can arrive at an accuracy rate of 74%. The trained model was also demonstrated on the test set generated by taking the background images with camera mounted on a station wagon and on a field test.

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Acknowledgement

This research was supported by National Key R&D Program of China (2018YFB1600303) and the Department of Transportation of Shandong Province (No. 2018BZ4). The authors are grateful to this financial supports.

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Authors and Affiliations

  1. School of Transportation Science and Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, 100191, Beijing, People’s Republic of China

    Siqi Zhou & Feng Li

  2. Shandong Provincial Communications Planning and Design Institute CO., LTD, Jinan, Shandong, 250031, People’s Republic of China

    Yufeng Bi

  3. Department of Electronic Engineering, Tsinghua University, Beijing, 100084, People’s Republic of China

    Xu Wei

  4. School of Naval Architecture, Shanghai Jiao Tong University, Ocean & Civil Engineering, Shanghai, 200240, People’s Republic of China

    Jiachen Liu

  5. School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Zixin Ye

  6. Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai, 201804, People’s Republic of China

    Yuchuan Du

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  1. Siqi Zhou
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Correspondence to Feng Li.

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Zhou, S., Bi, Y., Wei, X. et al. Automated detection and classification of spilled loads on freeways based on improved YOLO network. Machine Vision and Applications 32, 44 (2021). https://doi.org/10.1007/s00138-021-01171-z

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