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- Title
非局部稀疏关注的 YOLOv4 优化算法.
- Authors
闵锋; 毛一新; 侯泽铭; 杨朝源; 王名茂
- Abstract
Traditional target detection networks such as Fast R-CNN and ReseNet lose a large amount of spatial location information representation in the process of downsampling to extract image features, and have the problem of poor detection of smaller targets. On the basis of preserving spatial location information, a cascaded residual high resolution network CrHRnet(cascaded residual high resolution network)with non-local sparse attention is proposed. The network architecture starts from a high-resolution sub-network and gradually adds sub-networks from high to low resolution to form more stages, connects multiple resolution sub-networks in parallel, and a cascaded residual module(CrModule)is used for feature extraction between streams of same-resolution features; multi-scale feature map fusion is used to make each representation from high to low resolution repeatedly and information is received from other parallel representations to produce highresolution representations rich in semantic representations and spatial location representations; NLSA(non-local sparse attention)algorithm is introduced to realize the deep network feature block hyper-segmentation reconstruction, to explore the structural association between objects of different scales, to improve the feature representation of smaller objects to make them similar to larger objects, and to enhance the feature learnability of smaller targets. Extensive evaluation on the VOC2007 dataset shows that using CrHRnet as the backbone feature extraction network for YOLOv4 can effectively improve the accuracy of target detection; the CrHRnet-YOLOv4 test mAP(mean average precision)is 1.8, 9.5, and 3.4 percentage points higher than YOLOv4, YOLOv5_s, YOLOv5_m, respectively. Under the same hardware conditions, the frames per second (FPS) for single-image detection is increased by 30% compared to the YOLOv4 network.
- Subjects
INFORMATION processing; ALGORITHMS; FEATURE extraction; IMAGE reconstruction algorithms; PERCENTILES; HARDWARE
- Publication
Journal of Computer Engineering & Applications, 2023, Vol 59, Issue 21, p123
- ISSN
1002-8331
- Publication type
Article
- DOI
10.3778/j.issn.1002-8331.2207-0047