Research on Motion Blur Object Detection Technology for Imaging Guidance

doi:10.12382/bgxb.2024.0376

Abstract

Abstract:

To enhance the accuracy and efficiency of motion-blurred image object detection in missile-borne imaging guidance,this paper proposes a lighter and more effective motion-blurred image object detection (LEMBD) network.The causes of motion-blurred image are analyzed,and a dedicated motion-blurred image dataset is constructed based on the imaging mechanism.Without increasing network parameters,a shared-weight siamese network design is adopted,and the prior knowledge is introduced to extract the features of blurred images by the feature learning of clear images,thereby enabling the simultaneous detection of both clear and blurred images.Additionally,the partial depthwise separable convolutions are introduced to replace the standard convolutions,which significantly reduce the parameter count and computational cost while enhancing learning performance.To further improve the feature fusion quality,a cross-layer path aggregation feature pyramid network is designed to effectively leverage both the detail information of low-level features and the semantic information of high-level features.Experimental results demonstrate that the proposed LEMBD network achieves superior performance in detecting the targets within motion-blurred images compared to conventional object detection and state-of-the-art motion-blurred detection methods,which can provide more accurate relative positional information for precision guidance tasks.

Key words: accurate object detection, motion-blurring, lightweight, partial depth separable convolution, cross-layer path aggregation feature pyramid network

CLC Number:

TP391.41

ZHAO Chunbo, MO Bo, LI Dawei, ZHAO Jie. Research on Motion Blur Object Detection Technology for Imaging Guidance[J]. Acta Armamentarii, 2025, 46(2): 240376-.

Figures/Tables 14

References 21

[1]	李成, 李建勋, 童中翔, 等. 红外成像制导末端局部图像识别跟踪研究[J]. 兵工学报, 2015, 36(7):1213-1221. doi: 10.3969/j.issn.1000-1093.2015.07.009
	LI C, LI J X, TONG Z X, et al. Research on partial image recognition and tracking in infrared imaging terminal guidance[J]. Acta Armamentarii, 2015, 36(7):1213-1221. (in Chinese) doi: 10.3969/j.issn.1000-1093.2015.07.009
[2]	LIU W, ANGUELOV D, ERHAN D, et al. SSD:single shot multibox detector[C]// Proceedings of the Computer Vision-ECCV 2016:14th European Conference. Amsterdam,the Netherlands: Springer, 2016:21-37.
[3]	BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4:optimal speed and accuracy of object detection[R/OL]. Ithaca,NY,US: Cornell University, 2024(2024-04-23). https://arxiv.org/abs/2004.10934
[4]	LI C Y, LI L L, JIANG H L, et al. YOLOv6:a single-stage object detection framework for industrial applications[R/OL]. Ithaca,NY,US: Cornell University, 2022(2022-09-07). https://arxiv.org/abs/2209.02976
[5]	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[C]// Proceedings of the 2023 IEEE Conference on Computer Vision and Pattern Recognition. Vancouver,Canada,IEEE, 2023:7464-7475.
[6]	ZHOU L H, MIN W D, LIN D Y, et al. Detecting motion blurred vehicle logo in IoV using filter-DeblurGAN and VL-YOLO[J]. IEEE Transactions on Vehicular Technology, 2020, 69(4):3604-3614.
[7]	KUPYN O, BUDZAN V, MYKHAILYCH M, et al. DeblurGAN:blind motion deblurring using conditional adversarial networks[C]// Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City,UT,US, IEEE, 2018:8183-8192.
[8]	REDMON J, FARHADI A. YOLOv3:an incremental improvement:arXiv:1804.02767v1[R/OL]. Ithaca,NY,US: Cornell University, 2018(2018-04-08) [2022-05-17]. https://arXiv.org/abs/1804.02767v1
[9]	WANG H Z, HU C H, QIAN W J, et al. RT-Deblur:real-time image deblurring for object detection[J]. The Visual Computer, 2024, 40(4):2873-2887.
[10]	LIU B D, XU C, YANG W, et al. Motion robust high-speed light-weighted object detection with event camera[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72:1-13.
[11]	SAYED M, BROSTOW G. Improved handling of motion blur in online object detection[C]// Proceedings of the 2021 IEEE conference on computer vision and pattern recognition. Nashville,TN,US,IEEE, 2021:1706-1716.
[12]	HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas,NV,US,IEEE, 2016:770-778.
[13]	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 & Machine Intelligence, 2017, 39(6):1137-1149.
[14]	ZHAO Q, LIU B H, LYU S C, et al. TPH-YOLOv5++:boosting object detection on drone-captured scenarios with cross-layer asymmetric transformer[J]. Remote Sensing, 2023, 15(6):1687.
[15]	SUN Y M, CAO B, ZHU P F, et al. Drone-based RGB-infrared cross-modality vehicle detection via uncertainty-aware learning[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2022, 32(10):6700-6713.
[16]	ZHANG G J, LUO Z P, TIAN Z C, et al. Towards efficient use of multi-scale features in transformer-based object detectors[C]// Proceedings of the 2023 IEEE Conference on Computer Vision and Pattern Recognition.Vancouver,Canada, IEEE, 2023:6206-6216.
[17]	LIANG J Y, CAO J Z, FAN Y C, et al. VRT:a video restoration transformer[J]. IEEE Transactions on Image Processing, 2024, 33:2171-2182.
[18]	CHEN J R, KAO S H, HE H, et al. Run,don’t walk:chasing higher FLOPS for faster neural networks[C] // Proceedings of the 2023 IEEE Conference on Computer Vision and Pattern Recognition. Vancouver,Canada,IEEE, 2023:12021-12031.
[19]	KAISER L, GOMEZ A N, CHOLLET F. Depthwise separable convolutions for neural machine translation[R/OL]. Ithaca,NY,US: Cornell University, 2017(2017-06-16). https://arxiv.org/abs/1706.03059
[20]	BAO C, CAO J, HAO Q, et al. Dual-YOLO architecture from infrared and visible IMAGES for object detection[J]. Sensors, 2023, 23(6):2934.
[21]	YANG X, YAN J C. Arbitrary-oriented object detection with circular smooth label[C]// Computer Vision-ECCV 2020:16th European Conference.Glasgow,UK:Springer International Publishing, 2020:677-694.

CPU参数	数值	GPU参数	数值
Intel(R) Core(TM)	i9-7980XE	NVIDIA GeForce RTX	3090
基准速度	2.60GHz	内存	24.0GB
内核	18	Pytorch版本	1.10.1
逻辑处理器	36	CUDA版本	11.3

CPU参数	数值	GPU参数	数值
Intel(R) Core(TM)	i9-7980XE	NVIDIA GeForce RTX	3090
基准速度	2.60GHz	内存	24.0GB
内核	18	Pytorch版本	1.10.1
逻辑处理器	36	CUDA版本	11.3

PIDSFENet	PDS	EPAFPN	CLPAFPN	轿车	货车	卡车	公交车	厢式货车	召回率	mAP0.5/%	精确率	参数量/10⁶	消耗算力/10⁹
×	×	×	×	85.7	42.7	56.7	90.6	44.5	61.1	64.1	65.4	2.01	5.0
√	×	×	×	88.7	50.4	62.4	92.2	48.0	63.7^↑2.6	68.3^↑4.2	71.3^↑5.9	2.01	5.0
√	√	×	×	89.0	47.2	64.7	92.8	49.6	62.3^↑1.2	68.7^↑4.6	75.1^↑9.7	1.19^↓40.5%	3.3^↓36%
√	√	√	×	88.0	44.0	58.5	89.7	46.9	62.5^↑1.4	65.4^↑1.3	67.9^↑2.5	1.28^↓36%	6.4^↑28%
√	√	×	√	88.5	47.2	59.1	90.5	48.4	62.0^↑0.9	66.7^↑2.6	71.8^↑6.4	1.55^↓22.5%	8.1^↑62%

PIDSFENet	PDS	EPAFPN	CLPAFPN	轿车	货车	卡车	公交车	厢式货车	召回率	mAP0.5/%	精确率	参数量/10⁶	消耗算力/10⁹
×	×	×	×	85.7	42.7	56.7	90.6	44.5	61.1	64.1	65.4	2.01	5.0
√	×	×	×	88.7	50.4	62.4	92.2	48.0	63.7^↑2.6	68.3^↑4.2	71.3^↑5.9	2.01	5.0
√	√	×	×	89.0	47.2	64.7	92.8	49.6	62.3^↑1.2	68.7^↑4.6	75.1^↑9.7	1.19^↓40.5%	3.3^↓36%
√	√	√	×	88.0	44.0	58.5	89.7	46.9	62.5^↑1.4	65.4^↑1.3	67.9^↑2.5	1.28^↓36%	6.4^↑28%
√	√	×	√	88.5	47.2	59.1	90.5	48.4	62.0^↑0.9	66.7^↑2.6	71.8^↑6.4	1.55^↓22.5%	8.1^↑62%

算法	召回率	mAP0.5/ %	精确率	参数量/10⁶	消耗算力/10⁹	帧率/ (帧·s^-1)
Spec By Exposure	53.0	64.9		41.5	67.3	20.9
TPH-YOLOv5++	67.4	74.6	79.9	7.18	29.9	59.5
LEMBD	69.6	75.2	79.0	4.90	17.8	55.5