Lightweight and Real-Time Object Detection on Edge Devices: A Unified Framework for Resource-Constrained Environments
DOI:
https://doi.org/10.4108/eetismla.12814Keywords:
Edge Computing, Model Compression, Object Detection, Real-Time Processing, Resource-Constrained DevicesAbstract
Advances in edge computing have heightened the demand for object detection models capable of running efficiently on devices with constrained computational resources. This paper presents a robust hybrid detection framework that integrates template matching with Faster R-CNN to enhance detection accuracy in challenging conditions, such as occlusion, low lighting, and motion blur, while maintaining reasonable computational efficiency. Unlike conventional cloud-based detection, our approach reduces latency and improves data privacy. On the LASIESTA dataset, the proposed method achieves a mean Average Precision (mAP) of 88.2% at IoU 0.5 and 74.6% at IoU 0.75, outperforming Faster R-CNN by 4.3% in precision and 3.6% in recall. Although inference time increases modestly by 6 ms/frame compared to Faster R-CNN alone, the hybrid method consistently delivers superior robustness. While our implementation focuses on performance evaluation on a workstation, the framework design can be adapted for deployment on heterogeneous devices through additional optimization steps such as pruning and quantization. These findings demonstrate that combining classical localization techniques with deep learning models yields a practical and effective solution for real-time detection in resource-constrained environments.
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[1] Ren J, Wang Y. Overview of object detection algorithms using convolutional neural networks. Journal of Computer and Communications. 2022;10(1):115–32.
[2] 2. Chen W, Li Y, Tian Z, Zhang F. 2D and 3D object detection algorithms from images: A Survey. Array. 2023;100305.
[3] Bouguettaya A, Kechida A, TABERKIT AM. A survey on lightweight CNN-based object detection algorithms for platforms with limited computational resources. International Journal of Informatics and Applied Mathematics. 2019;2(2):28–44.
[4] Zhao R, Niu X, Wu Y, Luk W, Liu Q. Optimizing CNN-based object detection algorithms on embedded FPGA platforms. In: Applied Reconfigurable Computing: 13th International Symposium, ARC 2017, Delft, The Netherlands, April 3-7, 2017, Proceedings 13. Springer; 2017. p. 255–67.
[5] Chen C, Liu MY, Tuzel O, Xiao J. R-CNN for small object detection. In: Computer Vision–ACCV 2016: 13th Asian Conference on Computer Vision, Taipei, Taiwan, November 20-24, 2016, Revised Selected Papers, Part V 13. Springer; 2017. p. 214–30.
[6] Kumar A, Zhang ZJ, Lyu H. Object detection in real time based on improved single shot multi-box detector algorithm. EURASIP J Wirel Commun Netw. 2020;2020:1–18.
[7] Huang R, Pedoeem J, Chen C. YOLO-LITE: a real-time object detection algorithm optimized for non-GPU computers. In: 2018 IEEE international conference on big data (big data). IEEE; 2018. p. 2503–10.
[8] Wang J, Jiang S, Song W, Yang Y. A comparative study of small object detection algorithms. In: 2019 Chinese control conference (CCC). IEEE; 2019. p. 8507–12.
[9] Mahaur B, Singh N, Mishra KK. Road object detection: a comparative study of deep learning-based algorithms. Multimed Tools Appl. 2022;81(10):14247–82.
[10] Xiao Y, Tian Z, Yu J, Zhang Y, Liu S, Du S, et al. A review of object detection based on deep learning. Multimed Tools Appl. 2020;79:23729–91.
[11] Galteri L, Bertini M, Seidenari L, Del Bimbo A. Video compression for object detection algorithms. In: 2018 24th International Conference on Pattern Recognition (ICPR). IEEE; 2018. p. 3007–12.
[12] Zhao L, Li S. Object detection algorithm based on improved YOLOv3. Electronics (Basel). 2020;9(3):537.
[13] Li K, Cao L. A review of object detection techniques. In: 2020 5th International Conference on Electromechanical Control Technology and Transportation (ICECTT). IEEE; 2020. p. 385–90.
[14] Raghunandan A, Raghav P, Aradhya HVR. Object detection algorithms for video surveillance applications. In: 2018 International Conference on Communication and Signal Processing (ICCSP). IEEE; 2018. p. 563–8.
[15] Cuevas C, Yáñez EM, García N. Labeled dataset for integral evaluation of moving object detection algorithms: LASIESTA. Computer Vision and Image Understanding. 2016;152:103–17.
[16] Padilla R, Netto SL, Da Silva EAB. A survey on performance metrics for object-detection algorithms. In: 2020 international conference on systems, signals and image processing (IWSSIP). IEEE; 2020. p. 237–42.
[17] Zangana HM, Mustafa FM, Omar M. A Hybrid Approach for Robust Object Detection: Integrating Template Matching and Faster R-CNN. EAI Endorsed Transactions on AI and Robotics. 2024;3.
[18] Peng L, Wang H, Li J. Uncertainty evaluation of object detection algorithms for autonomous vehicles. Automotive Innovation. 2021;4(3):241–52.
[19] LUO H lan, CHEN H kun. Survey of object detection based on deep learning. Acta Electonica Sinica. 2020;48(6):1230.
[20] Amit Y, Felzenszwalb P, Girshick R. Object detection. In: Computer Vision: A Reference Guide. Springer; 2021. p. 875–83.
[21] Rajeshwari P, Abhishek P, Srikanth P, Vinod T. Object detection: an overview. Int J Trend Sci Res Dev(IJTSRD). 2019;3(1):1663–5.
[22] Yadav N, Binay U. Comparative study of object detection algorithms. International Research Journal of Engineering and Technology (IRJET). 2017;4(11):586–91.
[23] Waheed SR, Suaib NM, Rahim MSM, Adnan MM, Salim AA. Deep learning algorithms-based object detection and localization revisited. In: journal of physics: conference series. IOP Publishing; 2021. p. 012001.
[24] Li M, Zhu H, Chen H, Xue L, Gao T. Research on object detection algorithm based on deep learning. In: Journal of Physics: Conference Series. IOP Publishing; 2021. p. 012046.
[25] Du L, Zhang R, Wang X. Overview of two-stage object detection algorithms. In: Journal of Physics: Conference Series. IOP Publishing; 2020. p. 012033.
[26] Malhotra P, Garg E. Object detection techniques: a comparison. In: 2020 7th International Conference on Smart Structures and Systems (ICSSS). IEEE; 2020. p. 1–4.
[27] Haris M, Glowacz A. Road object detection: A comparative study of deep learning-based algorithms. Electronics (Basel). 2021;10(16):1932.
[28] Sun W, Dai L, Zhang X, Chang P, He X. RSOD: Real-time small object detection algorithm in UAV-based traffic monitoring. Applied Intelligence. 2021;1–16.
[29] Li Z, Du Y, Zhu M, Zhou S, Zhang L. A survey of 3D object detection algorithms for intelligent vehicles development. Artif Life Robot. 2022;1–8.
[30] Deng J, Xuan X, Wang W, Li Z, Yao H, Wang Z. A review of research on object detection based on deep learning. In: Journal of Physics: Conference Series. IOP Publishing; 2020. p. 012028.
[31] Zhou Y, Yang X, Zhang G, Wang J, Liu Y, Hou L, et al. Mmrotate: A rotated object detection benchmark using pytorch. In: Proceedings of the 30th ACM International Conference on Multimedia. 2022. p. 7331–4.
[32] John A, Meva D. A comparative study of various object detection algorithms and performance analysis. International Journal of Computer Sciences and Engineering. 2020;8(10):158–63.
[33] Zou X. A review of object detection techniques. In: 2019 International conference on smart grid and electrical automation (ICSGEA). IEEE; 2019. p. 251–4.
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