Abstract
Multi-Object detection in traffic scenarios plays a crucial role in ensuring the safety of people and property, as well as facilitating the smooth flow of traffic on roads. However, the existing algorithms are inefficient in detecting real scenarios due to the following drawbacks: (1) a scarcity of traffic scene datasets; (2) a lack of tailoring for specific scenarios; and (3) high computational complexity, which hinders practical use. In this paper, we propose a solution to eliminate these drawbacks. Specifically, we introduce a Full-Scene Traffic Dataset (FSTD) with Spatio-temporal features that includes multiple views, multiple scenes, and multiple objectives. Additionally, we propose the improved YOLOv7 model with redesigned BiFusion, NWD and SPPFCSPC modules (BNF-YOLOv7), which is a lightweight and efficient approach that addresses the intricacies of multi-object detection in traffic scenarios. BNF-YOLOv7 is achieved through several improvements over YOLOv7, including the use of the BiFusion feature fusion module, the NWD approach, and the redesign of the loss function. First, we improve the SPPCSPC structure to obtain SPPFCSPC, which maintains the same receptive field while achieving speedup. Second, we use the BiFusion feature fusion module to enhance feature representation capability and improve positional information of objects. Additionally, we introduce NWD and redesign the loss function to address the detection of tiny objects in traffic scenarios. Experiments on the FSTD and UA-DETRAC dataset show that BNF-YOLOv7 outperforms other algorithms with a 3.3% increase in mAP on FSTD and a 2.4% increase on UA-DETRAC. Additionally, BNF-YOLOv7 maintains significantly better real-time performance, increasing the FPS by 10% in real scenarios.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: YOLOv4: optimal speed and accuracy of object detection. arXiv:2004.10934 [cs, eess] (2020)
Cai, Z., Vasconcelos, N.: Cascade R-CNN: high quality object detection and instance segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 43(5), 1483–1498 (2019)
Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., Tian, Q.: Centernet: keypoint triplets for object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6569–6578 (2019)
Elfwing, S., Uchibe, E., Doya, K.: Sigmoid-weighted linear units for neural network function approximation in reinforcement learning. Neural Netw. 107, 3–11 (2018)
Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The kitti vision benchmark suite. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2012)
Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448–456. PMLR (2015)
Jocher, G.: YOLOv5 by Ultralytics (2020). https://doi.org/10.5281/zenodo.3908559, https://github.com/ultralytics/yolov5
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Kowol, K., Rottmann, M., Bracke, S., Gottschalk, H.: YOdar: uncertainty-based sensor fusion for vehicle detection with camera and radar sensors (2020). https://doi.org/10.48550/arXiv.2010.03320
Li, C., et al.: YOLOv6 v3.0: a full-scale reloading (2023).https://doi.org/10.48550/arXiv.2301.05586
Liang, M., Yang, B., Chen, Y., Hu, R., Urtasun, R.: Multi-task multi-sensor fusion for 3D object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7345–7353 (2019)
Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8759–8768 (2018)
Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Marriott, R.T., Romdhani, S., Chen, L.: A 3D GAN for improved large-pose facial recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13445–13455 (2021)
Qin, L., et al.: Id-yolo: real-time salient object detection based on the driver’s fixation region. IEEE Trans. Intell. Transp. Syst. 23(9), 15898–15908 (2022)
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)
Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263–7271 (2017)
Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. Adv. Neural Inf. Process. syst. 28 (2015)
Song, X., et al.: A survey on deep learning based knowledge tracing. Knowl.-Based Syst. 258, 110036 (2022)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Wang, C.Y., Bochkovskiy, A., Liao, H.Y.M.: Yolov7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv preprint arXiv:2207.02696 (2022)
Wang, C.Y., Liao, H.Y.M., Wu, Y.H., Chen, P.Y., Hsieh, J.W., Yeh, I.H.: CSPNet: a new backbone that can enhance learning capability of CNN. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 390–391 (2020)
Wang, F., Xu, J., Liu, C., Zhou, R., Zhao, P.: On prediction of traffic flows in smart cities: a multitask deep learning based approach. World Wide Web 24, 805–823 (2021)
Wang, J., Xu, C., Yang, W., Yu, L.: A normalized gaussian wasserstein distance for tiny object detection. arXiv preprint arXiv:2110.13389 (2021)
Wang, L., et al.: Model: motif-based deep feature learning for link prediction. IEEE Trans. Comput. Soc. Syst. 7(2), 503–516 (2020)
Wen, L., et al.: UA-DETRAC: a new benchmark and protocol for multi-object detection and tracking. Comput. Vis. Image Underst, 193, 102907 (2020)
Xu, C., et al.: Uncertainty-aware multi-view deep learning for internet of things applications. IEEE Trans. Industr. Inf. 19(2), 1456–1466 (2022)
Yang, X., Yan, J., Ming, Q., Wang, W., Zhang, X., Tian, Q.: Rethinking rotated object detection with gaussian wasserstein distance loss. In: International Conference on Machine Learning, pp. 11830–11841. PMLR (2021)
Yin, H., Yang, S., Song, X., Liu, W., Li, J.: Deep fusion of multimodal features for social media retweet time prediction. World Wide Web 24, 1027–1044 (2021)
Yu, F., et al.: Bdd100k: a diverse driving dataset for heterogeneous multitask learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2636–2645 (2020)
Yu, J., Jiang, Y., Wang, Z., Cao, Z., Huang, T.: Unitbox: an advanced object detection network. In: Proceedings of the 24th ACM International Conference on Multimedia, pp. 516–520 (2016)
Zhang, Wei, Gao, Xian-zhong, Yang, Chi-fu, Jiang, Feng, Chen, Zhi-yuan: A object detection and tracking method for security in intelligence of unmanned surface vehicles. J. Ambient Intell. Humanized Comput. 13(3), 1279–1291 (2020). https://doi.org/10.1007/s12652-020-02573-z
Zheng, Z., et al.: Enhancing geometric factors in model learning and inference for object detection and instance segmentation. IEEE Trans. Cybern. 52(8), 8574–8586 (2021)
Acknowledgments
This work was supported in part by the Major Key Project of PCL under Grant PCL2023A09 and PCL2022A03, Guangdong Major Project of Basic and Applied Basic Research under Grant 2019B030302002.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, K., Song, X., Sun, S., Zhao, J., Xu, C., Song, H. (2024). Efficient Multi-object Detection for Complexity Spatio-Temporal Scenes. In: Song, X., Feng, R., Chen, Y., Li, J., Min, G. (eds) Web and Big Data. APWeb-WAIM 2023. Lecture Notes in Computer Science, vol 14334. Springer, Singapore. https://doi.org/10.1007/978-981-97-2421-5_13
Download citation
DOI: https://doi.org/10.1007/978-981-97-2421-5_13
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-2420-8
Online ISBN: 978-981-97-2421-5
eBook Packages: Computer ScienceComputer Science (R0)