SF-YOLOv5: A Lightweight Small Object Detection Algorithm Based on Improved Feature Fusion Mode

In the research of computer vision, a very challenging problem is the detection of small objects. The existing detection algorithms often focus on detecting full-scale objects, without making proprietary optimization for detecting small-size objects. For small objects dense scenes, not only the accu...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 22; no. 15; p. 5817
Main Authors: Liu, Haiying, Sun, Fengqian, Gu, Jason, Deng, Lixia
Format: Journal Article
Language:English
Published: Basel MDPI AG 04.08.2022
MDPI
Subjects:
Ability tests
Accuracy
Algorithms
Computer vision
object detection
segmentation and categorization
small object
visual tracking
YOLO
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:In the research of computer vision, a very challenging problem is the detection of small objects. The existing detection algorithms often focus on detecting full-scale objects, without making proprietary optimization for detecting small-size objects. For small objects dense scenes, not only the accuracy is low, but also there is a certain waste of computing resources. An improved detection algorithm was proposed for small objects based on YOLOv5. By reasonably clipping the feature map output of the large object detection layer, the computing resources required by the model were significantly reduced and the model becomes more lightweight. An improved feature fusion method (PB-FPN) for small object detection based on PANet and BiFPN was proposed, which effectively increased the detection ability for small object of the algorithm. By introducing the spatial pyramid pooling (SPP) in the backbone network into the feature fusion network and connecting with the model prediction head, the performance of the algorithm was effectively enhanced. The experiments demonstrated that the improved algorithm has very good results in detection accuracy and real-time ability. Compared with the classical YOLOv5, the mAP@0.5 and mAP@0.5:0.95 of SF-YOLOv5 were increased by 1.6% and 0.8%, respectively, the number of parameters of the network were reduced by 68.2%, computational resources (FLOPs) were reduced by 12.7%, and the inferring time of the mode was reduced by 6.9%.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s22155817