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Multi-target vehicle detection based on corner pooling with attention mechanism

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Abstract

Multi-target detection based on corner pooling provides a distinctive framework without anchor boxes, which has achieved wide application in the area of intelligent transportation system. To effectively detect small vehicles in the distant view, we propose an improved detection network termed corner pooling with attention mechanism (CPAM). A newly devised network called Hourglass with Coordinate Attention(Hourglass-CA) is proposed as an alternative to the Hourglass-104 backbone network. This one incorporates a multi-level attention mechanism to optimize the efficiency of feature extraction. Additionally, a novel multi-level attention loss(MLA loss) is presented, which dynamically adjusts the offsets during the feature extraction process. The experimental results demonstrate that our proposed CPAM achieves lightweight detection, reducing the parameters from 201M to 117M with an FPS from 4.2 to 16.1. Moreover, the AP can reach 51.6\(\%\), surpassing several existing detectors.

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Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Yan C, Zhang H, Li X, Yuan D (2022) R-SSD: Refined single shot multibox detector for pedestrian detection. Appl Intell 52(9):10430–10447

    Article  Google Scholar 

  2. Hao LY, Yang Z, Liu YP, Shen C (2023) TRCA-Net: stronger U structured network for human image segmentation. Neural Comput Appl 35(13):9627–9635

    Article  Google Scholar 

  3. Li X, Kong D (2023) SRIF-RCNN: Sparsely represented inputs fusion of different sensors for 3D object detection. Appl Intell 53(5):5532–5553

    Google Scholar 

  4. Xiao J, Yang L, Zhong F, Chen H, Li X (2023) Robust anomaly-based intrusion detection system for in-vehicle network by graph neural network framework. Appl Intell 53(3):3183–3206

    Article  Google Scholar 

  5. Zhao B, Wang C, Fu Q, Han Z (2021) A novel pattern for infrared small target detection with generative adversarial network. IEEE Trans Geosci Remote Sens 59(5):4481–4492

    Article  Google Scholar 

  6. Pang D, Shan T, Li W, Ma P, Tao R (2020) Infrared dim and small target detection based on greedy bilateral factorization in image sequences. IEEE J Sel Top Appl Earth Obs Remote Sens 13:3394–3408

    Article  Google Scholar 

  7. Chadwick S, Maddern W, Newman P (2019) Distant vehicle detection using radar and vision, International Conference on Robotics and Automation (ICRA), Montreal, Canada, 8311-8317

  8. Gilroy S, Jones E, Glavin M (2019) Overcoming occlusion in the automotive environment-A review. IEEE Trans Intell Transp Syst 22(1):23–35

    Article  Google Scholar 

  9. Redmon J, Farhadi A (2018) Yolov3: An incremental improvement. arXiv preprint arXiv: 1804.02767

  10. Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: Towards realtime object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137–1149

    Article  Google Scholar 

  11. Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. IEEE Transactions on Computer Vision and Pattern Recognition(CVPR), Columbia, USA, 580-587

  12. Girshick R (2015) Fast R-CNN. IEEE Transactions on International Conference on Computer Vision(ICCV), Santiago, Chile, 1440–48

  13. Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: Towards realtime object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137–1149

  14. Hei L, Jia D (2020) CornerNet: Detecting objects as paired keypoints. IEEE Transactions on European Conference on Computer Vision(ECCV), Glasgow, UK, 642–656

  15. Redmon J, Farhadi A (2017) Yolo9000: Better, faster, stronger, IEEE Transactions on Computer Vision and Pattern Recognition(CVPR). Honolulu, USA, pp 6517–6525

    Google Scholar 

  16. Redmon J, Farhadi A (2017) Yolo9000: Better, faster, stronger, IEEE Transactions on Computer Vision and Pattern Recognition(CVPR). Honolulu, USA, pp 6517–6525

  17. Oh J, Lee Y, Yoo J, Kwo S (2022) Improved Feature-Based Gaze Estimation Using Self-Attention Module and Synthetic Eye Images. Sensors 22(11):4026

    Article  Google Scholar 

  18. Sun L, Cheng S, Zheng Y, Wu Z, Zhang J (2022) SPANet: Successive pooling attention network for semantic segmentation of remote sensing images. IEEE J Sel Top Appl Earth Obs Remote Sens 15:4045–4057

    Article  Google Scholar 

  19. Liu C, Yi Z, Huang B, Zhou Z, Fang S, Li X, Zhang Y, Wu X (2023) A Deep Learning Method Based on Triplet Network Using Self-Attention for Tactile Grasp Outcomes Prediction. IEEE Trans Instrum Meas 72:1–14

    Google Scholar 

  20. Tan S, Zhang L, Shu X, Wang Z (2023) A feature-wise attention module based on the difference with surrounding features for convolutional neural networks. Front Comput Sci 17(6):176338

    Article  Google Scholar 

  21. Tan S, Zhang L, Shu X, Wang Z (2023) A feature-wise attention module based on the difference with surrounding features for convolutional neural networks. Front Comput Sci 17(6):176338

  22. Newell A, Deng J (2017) Pixels to graphs by associative embedding. Adv Neural Inf Process 2172–2181

  23. Qin Z, Hanwen J, Qiyu D, Yuanhao Y, Long C, Qian W (2022) Robust Lane Detection From Continuous Driving Scenes Using Deep Neural Networks. IEEE Trans Veh Technol 69(1):41–54

    Google Scholar 

  24. Tabelini L, Berriel R, Paixao, Thiago M, Badue C (2021) Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR), Nashvile, TN, USA, 294–302

  25. Wang B, Wang G, Chan KL, Wang L (2014) Tracklet association with online target-specific metric learning, 2014 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR), Columbus, USA, 1234–1241

  26. Qin Z, Hanwen J, Qiyu D, Yuanhao Y, Long C, Qian W (2022) Robust Lane Detection From Continuous Driving Scenes Using Deep Neural Networks. IEEE Trans Veh Technol 69(1):41–54

    Google Scholar 

  27. Hao LY, Li J, Guo G (2020) A multi-target corner pooling-based neural network for vehicle detection. Neural Comput Appl 32(18):14497–14506

  28. Yuan Z, Li X, Wang Q. Exploring Multi-Level Attention and Semantic Relationship for Remote Sensing Image Captioning. IEEE Access, 8, 2608-2620

  29. Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Zitnick CL (2014) Microsoft coco: Common objects in context, 2014 Proceedings European Conference Computer Vision(ECCV). Zurich, Switzerland, pp 2117–2125

    Google Scholar 

  30. Wen L, Du D, Cai Z et al (2020) UA-DETRAC: A new benchmark and protocol for multi-object detection and tracking. Comput Vis Image Underst 193:102907

    Article  Google Scholar 

  31. Zhu Y, Zhao C, Wang J, Xu Z, Lu H (2017) CoupleNet: Coupling global structure with local parts for object detection, 2017 IEEE Transactions on International Conference on Computer Vision(ICCV), Venice, Italy, 4146-4154

  32. Dai J, Qi H, Xiong Y, Li Y, Zhang G, Hu H, Wei Y (2017) Deformable convolutional networks, 2017 IEEE Transactions on International Conference on Computer Vision(ICCV), Venice, Italy, 764–773

  33. Hao LY, Li J, Guo G (2020) A multi-target corner pooling-based neural network for vehicle detection. Neural Comput Appl 32(18):14497–14506

    Article  Google Scholar 

  34. Shen Z, Liu Z, Li J, Jiang YG, Xue X (2017) DSOD: Learning deeply supervised object detectors from scratch, 2017 IEEE Transactions on International Conference on Computer Vision(ICCV), Venice, Italy, 1937–1945

  35. Paszke A, Gross S, Chintala S, Chanan G, Yang E, Devito Z, Lin Z, Desmaison A, Antiga L, Lerer A (2017) Automatic differentiation in PyTorch, Workshop on Autodiff Decision Program, 1–4

  36. Wen L, Du D, Cai Z et al (2020) UA-DETRAC: A new benchmark and protocol for multi-object detection and tracking. Comput Vis Image Underst 193:102907

  37. Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Zitnick CL (2014) Microsoft coco: Common objects in context, 2014 Proceedings European Conference Computer Vision(ECCV). Zurich, Switzerland, pp 2117–2125

  38. King DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv: 1412.6980

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant Nos. 52171292, 51939001), the Outstanding Young Talent Program of Dalian (Grant No. 2022RJ05).

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Authors and Affiliations

  1. Marine Electrical Engineering College, Dalian Maritime University, Dalian, 116000, China

    Li-Ying Hao, Jia-Rui Yang, Yunze Zhang & Jian Zhang

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  1. Li-Ying Hao
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  2. Jia-Rui Yang
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  3. Yunze Zhang
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  4. Jian Zhang
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Contributions

Conceptualization: Li-Ying Hao; Methodology: Jia-Rui Yang; Writing - original draft preparation: Jian Zhang; Writing - review and editing: Yunze Zhang.

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Correspondence to Li-Ying Hao.

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Hao, LY., Yang, JR., Zhang, Y. et al. Multi-target vehicle detection based on corner pooling with attention mechanism. Appl Intell 53, 29128–29139 (2023). https://doi.org/10.1007/s10489-023-05084-4

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