Small Object Detection Based on Feature Fusion and Mixed Attention

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Small Object Detection Based on Feature Fusion and Mixed Attention

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[1]WEI Mingjun,WANG Mohan,LIU Yazhi,et al.Small Object Detection Based on Feature Fusion and Mixed Attention[J].Journal of Zhengzhou University (Engineering Science),2024,45(03):72-79.[doi:10. 13705/ j. issn. 1671-6833. 2024. 03. 001]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 45 Number of periods: 2024 03 Page number: 72-79 Column: Public date: 2024-04-20

Title:: Small Object Detection Based on Feature Fusion and Mixed Attention

Author(s):: WEI Mingjun¹; 2; WANG Mohan¹; LIU Yazhi¹; 2; LI Hui¹; 1. College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China; 2. Hebei Provincial Key Laboratory of Industrial Intelligent Perception, North China University of Science and Technology, Tangshan 063210, China

Keywords:: small target detection; attention mechanism; feature fusion; deep learning; real-time detection

CLC:: TP391. 4TP18

DOI:: 10. 13705/ j. issn. 1671-6833. 2024. 03. 001

Abstract:: To address to the low feature information, low detection rates, and high false rate and missing rate in the target detection task, a Tr-SSD algorithm based on multiscale feature fusion and a hybrid attention mechanism was proposed. Firstly, a Resnet50 residual network was utilized as the backbone network for the SSD algorithm to enhance its feature extraction capabilities. Secondly, a hybrid attention mechanism was designed and applied to the mid-scale feature maps of the network to enhance effective information within the feature maps and establish longrange dependencies between pieces of information. Finally, a FPN (feature pyramid network) structure was formed by using network layers centered around the Transformer instead of the original backbone network in the SSD algorithm, which fused feature information of different scales to more accurately locate small targets. Experimental results showed that the Tr-SSD algorithm achieved mAP values of 81. 9%, 87. 5%, and 88. 4% on the PASCAL VOC dataset, HRSID dataset, and RSOD remote sensing dataset, respectively. This represented an improvement of 4. 7 percentage points, 6. 8 percentage points, and 9. 2 percentage points compared to the original SSD algorithm. Moreover, the detection speed could meet the requirements for real-time detection.

References:: [1] LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot MultiBox detector[J]. Computer Vision,2016,9905: 21-37.

[2] FU C Y, LIU W, RANGA A, et al. DSSD: deconvolutional single shot detector [ EB/ OL]. ( 2017 - 01 - 23)[2023-09-17]. https:∥arxiv. org/ abs/ 1701. 06659.

[3] LIN T Y, DOLLÁR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE, 2017: 936-944.

[4] TAN M X, PANG R M, LE Q V. EfficientDet: scalable and efficient object detection[C]∥2020 IEEE/ CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE, 2020: 10778-10787.

[5] QIAO S Y, CHEN L C, YUILLE A. DetectoRS: detecting objects with recursive feature pyramid and switchable atrous convolution[C]∥2021 IEEE/ CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE, 2021: 10208-10219.

[6] LIM J S, ASTRID M, YOON H J, et al. Small object detection using context and attention[C]∥2021 International Conference on Artificial Intelligence in Information and Communication ( ICAIIC ). Piscataway: IEEE, 2021: 181-186.

[7] YANG C, HUANG Z H, WANG N Y. QueryDet: cascaded sparse query for accelerating high-resolution small object detection[C]∥2022 IEEE/ CVF Conference on Computer Vision and Pattern Recognition ( CVPR). Piscataway: IEEE, 2022: 13658-13667.

[8] REDMON J,FARHADI A. YOLOv3:an incremental improvement[EB/ OL]. (2018- 04- 08) [2023- 09- 17].https:∥arxiv. org/ abs/ 1804. 02767.

[9] 马学森, 马吉, 蒋功辉, 等. 基于注意力机制和多尺度特征融合的绝缘子缺陷检测方法[J]. 南京大学学报(自然科学), 2022, 58(6): 1020-1029.

MA X S, MA J, JIANG G H, et al. Insulator defect detection method based on attention mechanism and multiscale feature fusion [ J]. Journal of Nanjing University (Natural Science), 2022, 58(6): 1020-1029.

[10] QU Z, HAN T Q, YI T M. MFFAMM: a small object detection with multi-scale feature fusion and attention mechanism module [ J]. Applied Sciences, 2022, 12 (18): 8940.

[11] NI J, WANG R, TANG J. ADSSD: improved single-shot detector with attention mechanism and dilated convolution [J]. Applied Sciences, 2023, 13(6): 4038.

[12] SHAN D R, XU Y L, ZHANG P, et al. DPSSD: dual-path single-shot detector[J]. Sensors, 2022, 22(12): 4616.

[13] SHI H, CHAI B Q, WANG Y P, et al. A local-sparseinformation- aggregation transformer with explicit contour guidance for SAR ship detection[ J]. Remote Sensing, 2022, 14(20): 5247.

[14] ZHANG X S, WAN F, LIU C, et al. Learning to match anchors for visual object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44 (6): 3096-3109.

[15] SHI H, FANG Z H, WANG Y P, et al. An adaptive sample assignment strategy based on feature enhancement for ship detection in SAR images[J]. Remote Sensing, 2022, 14(9): 2238.

[16] YU J M, WU T, ZHANG X, et al. An efficient lightweight SAR ship target detection network with improved regression loss function and enhanced feature information expression[J]. Sensors, 2022, 22(9): 3447.

[17] LIU M J, WANG X H, ZHOU A J, et al. UAV-YOLO: small object detection on unmanned aerial vehicle perspective[J]. Sensors, 2020, 20(8): 2238.

[18] HUANG Z C, WANG J L, FU X S, et al. DC-SPP-YOLO:dense connection and spatial pyramid pooling based YOLO for object detection [ J]. Information Sciences,2020, 522: 241-258.

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Last Update: 2024-04-29