Improved Object Detection Method Utilizing YOLOv7-Tiny for Unmanned Aerial Vehicle Photographic Imagery

In unmanned aerial vehicle photographs, object detection algorithms encounter challenges in enhancing both speed and accuracy for objects of different sizes, primarily due to complex backgrounds and small objects. This study introduces the PDWT-YOLO algorithm, based on the YOLOv7-tiny model, to impr...

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Bibliographic Details
Published in:Algorithms Vol. 16; no. 11; p. 520
Main Authors: Zhang, Linhua, Xiong, Ning, Pan, Xinghao, Yue, Xiaodong, Wu, Peng, Guo, Caiping
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.11.2023
Subjects:
Accuracy
Aerial photography
Aerial vehicle
Algorithms
Antennas
Boxes
Classification
Datasets
decoupled head
Deep learning
Drone aircraft
Effectiveness
Image enhancement
Loss functions
Methods
Neural networks
Object detection
Object detection method
Object recognition
Objects detection
Photographic imagery
Small object detection
Small objects
Telematics
Unmanned aerial vehicles
Unmanned aerial vehicles (UAV)
Weather
WIoU
Wise intersection over union
YOLOv7-tiny model
ISSN:1999-4893, 1999-4893
Online Access:Get full text
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Summary:In unmanned aerial vehicle photographs, object detection algorithms encounter challenges in enhancing both speed and accuracy for objects of different sizes, primarily due to complex backgrounds and small objects. This study introduces the PDWT-YOLO algorithm, based on the YOLOv7-tiny model, to improve the effectiveness of object detection across all sizes. The proposed method enhances the detection of small objects by incorporating a dedicated small-object detection layer, while reducing the conflict between classification and regression tasks through the replacement of the YOLOv7-tiny model’s detection head (IDetect) with a decoupled head. Moreover, network convergence is accelerated, and regression accuracy is improved by replacing the Complete Intersection over Union (CIoU) loss function with a Wise Intersection over Union (WIoU) focusing mechanism in the loss function. To assess the proposed model’s effectiveness, it was trained and tested on the VisDrone-2019 dataset comprising images captured by various drones across diverse scenarios, weather conditions, and lighting conditions. The experiments show that mAP@0.5:0.95 and mAP@0.5 increased by 5% and 6.7%, respectively, with acceptable running speed compared with the original YOLOv7-tiny model. Furthermore, this method shows improvement over other datasets, confirming that PDWT-YOLO is effective for multiscale object detection.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1999-4893
1999-4893
DOI:10.3390/a16110520