Abstract
Deep learning-based object detection methods demonstrated promising results. In reality, most methods suffer while running on edge devices due to their extensive network architecture and low inference speed. Additionally, there is a lack of industrial scenarios in the existing person, helmet, and head detection datasets. This research presents an efficient tiny network (ETN) for object detection that can perform on edge devices with high inference speed. We take the YOLOv5s model as our base model. We compress the YOLOv5s object detection model and minimize the computation redundancy, and propose two lightweight C3 modules (MC3 and SC3). Additionally, we construct two novel datasets: H2 (consists of safety helmet and head) and Person104K (consists of person) that fill the gaps in the earlier datasets with various industrial scenarios. We implemented and tested our method on Person104K and H2 datasets and achieved about 50.6% higher inference speed than the original YOLOv5s without compromising the accuracy. On the Nvidia Jetson AGX edge device, ETN achieves 42% higher FPS compared to the original YOLOv5s. Code is available at https://github.com/mdhosen/ETN.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agarap, A.F.: Deep learning using rectified linear units (ReLU). arXiv preprint arXiv:1803.08375 (2018)
Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: YOLOv4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)
Clevert, D.A., Unterthiner, T., Hochreiter, S.: zheng2020distance and accurate deep network learning by exponential linear units (elus). arXiv preprint arXiv:1511.07289 (2015)
Elfwing, S., Uchibe, E., Doya, K.: Sigmoid-weighted linear units for neural network function approximation in reinforcement learning. Neural Netw. 107, 3–11 (2018)
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., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)
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)
Howard, A., et al.: Searching for mobilenetv3. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1314–1324 (2019)
Jha, S., Seo, C., Yang, E., Joshi, G.P.: Real time object detection and tracking system for video surveillance system. Multimed. Tools Appl. 80(3), 3981–3996 (2021)
Jocher, G.: ultralytics/yolov5: v6.0 – YOLOv5n Nano models, Roboflow integration, TensorFlow export, OpenCV DNN support, October 2021. https://doi.org/10.5281/zenodo.5563715
Jung, H.K., Choi, G.S.: Improved YOLOv5: efficient object detection using drone images under various conditions. Appl. Sci. 12(14), 7255 (2022)
Köksal, A., Tuzcuoğlu, Ö., İnce, K.G., Ataseven, Y., Alatan, A.A.: Improved hard example mining approach for single shot object detectors. In: 2022 IEEE International Conference on Image Processing (ICIP), pp. 3536–3540. IEEE (2022)
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)
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
Ma, N., Zhang, X., Liu, M., Sun, J.: Activate or not: learning customized activation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8032–8042 (2021)
Muniswamaiah, M., Agerwala, T., Tappert, C.C.: A survey on cloudlets, mobile edge, and fog computing. In: 2021 8th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2021 7th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom), pp. 139–142. IEEE (2021)
Qiu, S., Xu, X., Cai, B.: FReLU: flexible rectified linear units for improving convolutional neural networks. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 1223–1228. IEEE (2018)
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)
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)
Yu, X., Gong, Y., Jiang, N., Ye, Q., Han, Z.: Scale match for tiny person detection. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1257–1265 (2020)
Zhang, H., Wang, Y., Dayoub, F., Sunderhauf, N.: VarifocalNet: an IoU-aware dense object detector. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8514–8523 (2021)
Acknowledgements
This work is supported by Cozum Makina Corporation.
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 Switzerland AG
About this paper
Cite this paper
Imani, H., Hosen, M.I., Feryad, V., Akyol, A. (2024). Efficient Object Detection Model for Edge Devices. In: Ortis, A., Hameed, A.A., Jamil, A. (eds) Advanced Engineering, Technology and Applications. ICAETA 2023. Communications in Computer and Information Science, vol 1983. Springer, Cham. https://doi.org/10.1007/978-3-031-50920-9_7
Download citation
DOI: https://doi.org/10.1007/978-3-031-50920-9_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-50919-3
Online ISBN: 978-3-031-50920-9
eBook Packages: Computer ScienceComputer Science (R0)