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The Application of Improved YOLO V3 in Multi-Scale Target Detection
Ju MR(鞠默然)1,2,3,4,5; Luo HB(罗海波)1,2,4,5; Wang ZB(王仲博)1,2,3,4,5; Hui B(惠斌)1,2,4,5; Chang Z(常铮)1,2,4,5
Department光电信息技术研究室
Source PublicationAPPLIED SCIENCES-BASEL
ISSN2076-3417
2019
Volume9Issue:18Pages:1-14
Indexed BySCI
WOS IDWOS:000489115200135
Contribution Rank1
Keywordtarget detection YOLO V3 anchor box computer vision deep learning
Abstract

Target detection is one of the most important research directions in computer vision. Recently, a variety of target detection algorithms have been proposed. Since the targets have varying sizes in a scene, it is essential to be able to detect the targets at different scales. To improve the detection performance of targets with different sizes, a multi-scale target detection algorithm was proposed involving improved YOLO (You Only Look Once) V3. The main contributions of our work include: (1) a mathematical derivation method based on Intersection over Union (IOU) was proposed to select the number and the aspect ratio dimensions of the candidate anchor boxes for each scale of the improved YOLO V3; (2) To further improve the detection performance of the network, the detection scales of YOLO V3 have been extended from 3 to 4 and the feature fusion target detection layer downsampled by 4x is established to detect the small targets; (3) To avoid gradient fading and enhance the reuse of the features, the six convolutional layers in front of the output detection layer are transformed into two residual units. The experimental results upon PASCAL VOC dataset and KITTI dataset show that the proposed method has obtained better performance than other state-of-the-art target detection algorithms.

Language英语
WOS SubjectChemistry, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied
WOS KeywordVEHICLE DETECTION ; IMAGERY
WOS Research AreaChemistry ; Materials Science ; Physics
Citation statistics
Cited Times:8[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/25771
Collection光电信息技术研究室
Corresponding AuthorJu MR(鞠默然)
Affiliation1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Institutes for Robotics and IntelligentManufacturing, Chinese Academy of Sciences, Shenyang 110169, China
3.University of Chinese Academy of Sciences, Beijing 100049, China
4.Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Science, Shenyang 110016, China
5.The Key Lab of Image Understanding and Computer Vision, Shenyang 110016, China
Recommended Citation
GB/T 7714
Ju MR,Luo HB,Wang ZB,et al. The Application of Improved YOLO V3 in Multi-Scale Target Detection[J]. APPLIED SCIENCES-BASEL,2019,9(18):1-14.
APA Ju MR,Luo HB,Wang ZB,Hui B,&Chang Z.(2019).The Application of Improved YOLO V3 in Multi-Scale Target Detection.APPLIED SCIENCES-BASEL,9(18),1-14.
MLA Ju MR,et al."The Application of Improved YOLO V3 in Multi-Scale Target Detection".APPLIED SCIENCES-BASEL 9.18(2019):1-14.
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