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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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References

  1. Cheu, R.L.: Neural network models for automated detection of lane-blocking incidents on freeways. Recent Doctoral Dissertations, University of California, Irvine, p. 60 (1994)

  2. Das, S., Kamenica, E., Mirka, R.: Reducing congestion through information design. Commun Control Comput. (2018). https://doi.org/10.1109/ALLERTON.2017.8262884

    Article  Google Scholar 

  3. Liang, Q., Zhou, M., Luan, W.: A dynamic road incident information delivery strategy to reduce urban traffic congestion. IEEE/CAA J. Automatica Sinica 5(05), 52–63 (2018)

    MathSciNet  Google Scholar 

  4. Ullman, G.L., Ogden, M.A.: Analysis of Major Freeway Incidents in Houston, Texas. Transp. Res. Rec. 1554(1), 221–227 (1996). https://doi.org/10.1177/0361198196155400126

    Article  Google Scholar 

  5. Cheng, X., Wenfang, L., Enxiang, L., Dan, G.: Highway traffic incident detection based on BPNN. Procedia Eng. 7, 482–489 (2010)

    Article  Google Scholar 

  6. Ren, J., Chen, Y., Xin, L., Shi, J., Mahama, H.: Detecting and locating of traffic incidents in a road segment based on lane-changing characteristics. Transp. A Transp. Sci. 13, 853–873 (2017)

    Google Scholar 

  7. Ren, J., Chen, Y., Xin, L., Shi, J., Li, B., Liu, Y.: Detecting and positioning of traffic incidents via video-based analysis of traffic states in a road segment. IET Intel. Transport Syst. 10(6), 428–437 (2016)

    Article  Google Scholar 

  8. Srinivasan, D., Wee Hoon, L., Ruey Long, C.: Traffic incident detection using particle swarm optimization. In: Proceedings of the 2003 IEEE Swarm Intelligence Symposium, SIS'03 (Cat. No.03EX706), pp. 144–151 (2003)

  9. Xu, S., Li, S., Wen, R.: Sensing and detecting traffic events using geosocial media data: a review. Comput. Environ. Urban Syst. 72, 146–160 (2018)

    Article  Google Scholar 

  10. Li, H., Shen, J.: Detection study of abandoning and scattering objects in real-time background. J. Chengdu Univ. (Nat. Sci. Ed.) 30(4), 343–344 (2011)

    Google Scholar 

  11. Wang, L., Qian, H.: Study of the obstacle detection based on active contour models. Control Eng. China 20, 202–205 (2013)

    Google Scholar 

  12. Miled, W., Pesquet, J.C., Parent, M.: Robust obstacle detection based on dense disparity maps. In: Moreno Díaz, R., Pichler, F., Quesada Arencibia, A. (eds.) Computer Aided Systems Theory—EUROCAST 2007. EUROCAST 2007. Lecture Notes in Computer Science, vol. 4739. Springer, Berlin (2007). https://doi.org/https://doi.org/10.1007/978-3-540-75867-9_143

  13. Demonceaux, C., Kachi-Akkouche, D.: Robust obstacle detection with monocular vision based on motion analysis. In: IEEE Intelligent Vehicles Symposium, 2004, Parma, Italy, pp. 527–532 (2004)

  14. Sinha, D., Feroz, F.: Obstacle detection on railway tracks using vibration sensors and signal filtering using Bayesian analysis. IEEE Sens. J. 16(3), 642–649 (2016)

    Article  Google Scholar 

  15. Kodagoda, S.: Stereo vision for obstacle detection in autonomous vehicle navigation. IOP Publishing ScholarBank@NUS Repository. https://scholarbank.nus.edu.sg/handle/10635/21024 (2010). Accessed 1 Nov 2020

  16. Zhang, A., Wang, K.C.P., Fei, Y., Liu, Y., Tao, S., Chen, C., Li, J., Q., Li, B.: Deep learning–based fully automated pavement crack detection on 3D asphalt surfaces with an improved CrackNet. J. Comput. Civ. Eng 32(5), 04018041 (2018)

  17. Kırcalı, D., Tek, F.B.: Ground plane detection using an RGB-D sensor. In: Czachórski, T., Gelenbe, E., Lent R. (eds.) Information Sciences and Systems 2014. Springer, Cham (2014) https://doi.org/https://doi.org/10.1007/978-3-319-09465-6_8

  18. Broggi, A., Caraffi, C., Porta, P.P., Zani, P.: The single frame stereo vision system for reliable obstacle detection used during the 2005 DARPA grand challenge on TerraMax. In: IEEE Intelligent Transportation Systems Conference 2006, pp. 745–752

  19. Eisapour Moghaddam, A., Akbarizadeh, G., Kaabi, H.: Automatic detection and segmentation of blood vessels and pulmonary nodules based on a line tracking method and generalized linear regression model. SIViP 13, 457–464 (2019)

  20. Samadi, F., Akbarizadeh, G., Kaabi, H.: Change detection in SAR images using deep belief network: a new training approach based on morphological images. IET Image Proc. 13(12), 2255–2264 (2019)

    Article  Google Scholar 

  21. Sharifzadeh, F., Akbarizadeh, G., Seifi Kavian, Y.: Ship classification in SAR images using a new hybrid CNN–MLP classifier. J. Indian Soc. Remote Sens. 47, 551–562 (2019)

    Article  Google Scholar 

  22. Taibi, F., Akbarizadeh, G., Farshidi, E.: Robust reservoir rock fracture recognition based on a new sparse feature learning and data training method. Multidimension. Syst. Signal Process. 30(4), 2113–2146 (2019)

    Article  Google Scholar 

  23. Zalpour, M., Akbarizadeh, G., Alaei-Sheini, N.: A new approach for oil tank detection using deep learning features with control false alarm rate in high-resolution satellite imagery. Int. J. Remote Sens. 41(6), 2239–2262 (2020)

    Article  Google Scholar 

  24. Lu, J., Chen, S., Wang, W., van Zuylen, H.: Applications a hybrid model of partial least squares and neural network for traffic incident detection. Expert Syst. Appl. 39(5), 4775–4784 (2012)

    Article  Google Scholar 

  25. Chakraborty, M.: Artificial neural network for performance modeling and optimization of CMOS analog circuits. Preprint at arXiv:1212.0215 (2012)

  26. Arslan, E., Orman, Z.: Road traffic analysis on the CNN universal machine. In: Proceedings of the World Congress on Engineering and Computer Science, vol. 1 (2011)

  27. Faro, A., Giordano, D., Spampinato, C.: Evaluation of the traffic parameters in a metropolitan area by fusing visual perceptions and CNN processing of webcam images. IEEE Trans. Neural Netw. 19(6), 1108–1129 (2008)

    Article  Google Scholar 

  28. Pan, X., Shi, J., Luo, P., Wang, X., Tang, X.: Spatial as deep: spatial CNN for traffic scene understanding. Preprint at arXiv:1712.06080 (2017)

  29. Guan, W., Xianghua, M.: Traffic police gesture recognition using RGB-D and faster R-CNN. In: 2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) (2018)

  30. Mhalla, A., Chateau, T., Maamatou, H., Gazzah, S., Ben Amara, N.E.: SMC faster R-CNN: Toward a scene-specialized multi-object detector. Comput. Vis. Image Underst. 164, 3–15 (2017)

    Article  Google Scholar 

  31. Cheng, P., Liu, W., Zhang, Y., Ma, H. LOCO: local context based faster R-CNN for small traffic sign detection. In: Schoeffmann, K. et al. (eds.) MultiMedia Modeling, MMM 2018. Lecture Notes in Computer Science, vol. 10704. Springer, Cham (2018)

  32. Artamonov, N.S., Yakimov, P.Y.: Towards real-time traffic sign recognition via YOLO on a mobile GPU. J. Phys. Conf. Ser. 1096, 012086 (2018)

    Article  Google Scholar 

  33. Jing, T., Wang, H., Zhang, X., Li, X., Yang, H.: An object detection system based on YOLO in traffic scene. In: 2017 6th International Conference on Computer Science and Network Technology (ICCSNT) (2018)

  34. Wei, Y., Ji, Z., Wang, H., Zhang, Z.: A vehicle real-time detection algorithm based on YOLOv2 framework. In: Real-time Image & Video Processing 2018 10670, 106700N (2018)

  35. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)

  36. Chakraborty, P., Adu-Gyamfi, Y.O., Poddar, S., Ahsani, V., Sharma, A., Sarkar, S.: Traffic congestion detection from camera images using deep convolution neural networks. Transp. Res. Rec. 2672(45), 222–231 (2018) . https://doi.org/10.1177/0361198118777631

  37. Deng, J., Dong, W., Socher, R., Li, L.J., Li, F.F.: ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision & Pattern Recognition 2009

  38. Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the KITTI dataset. Int. J. Robotics Res. 32(11), 1231–1237 (2013)

    Article  Google Scholar 

  39. Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016, pp. 3213–3223

  40. Ros, G., Sellart, L., Materzynska, J., Vazquez, D., Lopez, A.M.: The synthia dataset: a large collection of synthetic images for semantic segmentation of urban scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016, pp. 3234–3243

  41. Xu, H., Gao, Y., Yu, F., Darrell, T.: End-to-end learning of driving models from large-scale video datasets. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017, pp. 2174–2182

  42. Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft COCO: common objects in context. In: European Conference on Computer Vision, pp. 740–755. Springer, Cham (2014)

  43. Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The Pascal visual object classes (VOC) challenge. Int. J. Comput. Vision 88(2), 303–338 (2010)

    Article  Google Scholar 

  44. Jung, H., Choi, M.-K., Jung, J., Lee, J.-H., Kwon, S., Young Jung, W.: ResNet-based vehicle classification and localization in traffic surveillance systems. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops 2017, pp. 61–67

  45. Neubeck, A., Gool, V.L.: Efficient non-maximum suppression. In: 18th international conference on pattern recognition (ICPR'06), Hong Kong, pp. 850–855 (2006)

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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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