Object Detection Algorithm Based on YOLOv3 Model to Detect Occluded Targets

Target detection has experienced a rapid development stage from the concept to the practical application, the core technology has been broken through, many difficult problems have been solved, and it has been widely used in various fields. However, the existing target detection models still have som...

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Vydáno v:Journal of physics. Conference series Ročník 1881; číslo 4; s. 42043
Hlavní autor: Zhang, Dawei
Médium: Journal Article
Jazyk:angličtina
Vydáno: Bristol IOP Publishing 01.04.2021
Témata:
Accuracy
Algorithms
Model accuracy
Model testing
Multiple target tracking
Object recognition
Optimization
Physics
Target detection
ISSN:1742-6588, 1742-6596
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Shrnutí:Target detection has experienced a rapid development stage from the concept to the practical application, the core technology has been broken through, many difficult problems have been solved, and it has been widely used in various fields. However, the existing target detection models still have some technical problems such as incomplete detection and low accuracy. Therefore, this paper proposes a target detection algorithm based on V3 model to detect occluded targets. This paper makes an in-depth investigation and Research on the existing target detection model, analyzes the shortcomings of the current traditional target detection model through the test results, and the advantages of the network detection model based on YOLOv3 in target detection. Aiming at the main problems existing in the current detection technology, this paper puts forward the optimization and improvement scheme. By reconstructing the whole framework of multi-target tracking algorithm, optimizing the feature fusion algorithm, and improving the NMS algorithm, the scheme greatly improves the accuracy of detecting the occluded objects. The simulation results show that the improved scheme can improve the accuracy by 12.3% compared with the traditional scheme. The analysis shows that the target detection model based on YOLOv3 network can effectively improve the detection accuracy of the model.
Bibliografie:ObjectType-Conference Proceeding-1
SourceType-Scholarly Journals-1
content type line 14
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1881/4/042043