• Laser & Optoelectronics Progress
  • Vol. 59, Issue 4, 0415004 (2022)
Yujie Luo, Jian Zhang*, Liang Chen, Lü Zhang, Wanqing Ouyang, Daiqin Huang, and Yuyi Yang
Author Affiliations
  • School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan , Hunan 411100, China
  • show less
    DOI: 10.3788/LOP202259.0415004 Cite this Article Set citation alerts
    Yujie Luo, Jian Zhang, Liang Chen, Lü Zhang, Wanqing Ouyang, Daiqin Huang, Yuyi Yang. Lightweight Target Detection Algorithm Based on Adaptive Spatial Feature Fusion[J]. Laser & Optoelectronics Progress, 2022, 59(4): 0415004 Copy Citation Text show less
    References

    [1] Luo H L, Chen H K. Survey of object detection based on deep learning[J]. Acta Electronica Sinica, 48, 1230-1239(2020).

    [2] Girshick R, Donahue J, Darrell T et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C], 580-587(2014).

    [3] Girshick R. Fast R-CNN[C], 1440-1448(2015).

    [4] Ren S Q, He K M, Girshick R et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2017).

    [5] Liu W, Anguelov D, Erhan D et al. SSD: single shot MultiBox detector[M]. Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016, 9905, 21-37(2016).

    [6] Redmon J, Divvala S, Girshick R et al. You only look once: unified, real-time object detection[C], 779-788(2016).

    [7] Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C], 6517-6525(2017).

    [8] Redmon J, Farhadi A. YOLOV3: an incremental improvement[EB/OL]. https://arxiv.org/abs/1804.02767

    [9] Bochkovskiy A, Wang C Y, Liao H Y Mark. YOLOv4: optimal speed and accuracy of object detection[EB/OL]. https://arxiv.org/abs/2004.10934

    [10] Li B, Wang C, Wu J et al. Surface defect detection of aeroengine components based on improved YOLOv4 algorithm[J]. Laser & Optoelectronics Progress, 58, 1415004(2021).

    [11] Liu S, Qi L, Qin H F et al. Path aggregation network for instance segmentation[C], 8759-8768(2018).

    [12] Mao Q C, Sun H M, Liu Y B et al. Mini-YOLOv3: real-time object detector for embedded applications[J]. IEEE Access, 7, 133529-133538(2019).

    [13] Zhao H P, Zhou Y, Zhang L et al. Mixed YOLOv3-LITE: a lightweight real-time object detection method[J]. Sensors, 20, 1861(2020).

    [14] Huang R, Pedoeem J, Chen C X. YOLO-LITE: a real-time object detection algorithm optimized for non-GPU computers[C], 2503-2510(2018).

    [15] Xiao D, Shan F, Li Z et al. A target detection model based on improved tiny-Yolov3 under the environment of mining truck[J]. IEEE Access, 7, 123757-123764(2019).

    [16] Huang R, Gu J, Sun X et al. A rapid recognition method for electronic components based on the improved YOLO-V3 network[J]. Electronics, 8, 825(2019).

    [17] Liu W J, Gao M Y, Qu H C et al. Light-weight multi-object detection network based on inverted residual structure[J]. Laser & Optoelectronics Progress, 56, 221003(2019).

    [18] Ma Q, Zhu B, Zhang H W et al. Low-altitude UAV detection and recognition method based on optimized YOLOv3[J]. Laser & Optoelectronics Progress, 56, 201006(2019).

    [19] Guo J X, Liu L B, Xu F et al. Airport scene aircraft detection method based on YOLO v3[J]. Laser & Optoelectronics Progress, 56, 191003(2019).

    [20] Adarsh P, Rathi P, Kumar M. YOLO v3-Tiny: object detection and recognition using one stage improved model[C], 687-694(2020).

    [21] Howard A G, Zhu M L, Chen B et al. MobileNets: efficient convolutional neural networks for mobile vision applications[EB/OL]. https://arxiv.org/abs/1704.04861

    [22] Liu S T, Huang D, Wang Y H. Learning spatial fusion for single-shot object detection[EB/OL]. https://arxiv.org/abs/1911.09516

    Yujie Luo, Jian Zhang, Liang Chen, Lü Zhang, Wanqing Ouyang, Daiqin Huang, Yuyi Yang. Lightweight Target Detection Algorithm Based on Adaptive Spatial Feature Fusion[J]. Laser & Optoelectronics Progress, 2022, 59(4): 0415004
    Download Citation