Remote Sensing Image Object Detection Based on Improved YOLOv5

Aiming at the problems of complex background, high difficulty of small target detection and high miss detection rate in target detection of satellite remote sensing images, this paper proposes a multiscale target detection model based on YOLOv5 network with attention mechanism. -The feature extracti...

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Bibliographic Details
Published in2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) pp. 227 - 232
Main Authors Zhou, Shenglan, Guo, Rongrong, Zhang, Jianhua, Chen, Weilong, Peng, Yujia, Tong, Yushen, Dai, Yuebao
Format Conference Proceeding
LanguageEnglish
Published IEEE 26.05.2023
Subjects
attention mechanism
Convolution
deep learning
Feature extraction
Head
neural network
Object detection
Real-time systems
remote Sensing
Satellites
Sensors
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Summary:Aiming at the problems of complex background, high difficulty of small target detection and high miss detection rate in target detection of satellite remote sensing images, this paper proposes a multiscale target detection model based on YOLOv5 network with attention mechanism. -The feature extraction capability of the backbone network is enhanced by fusing efficient channel attention modules in the backbone network, and the detection head is decoupled and parallel convolution is used to perform classification and regression tasks separately to alleviate the conflict between classification and regression tasks. After experimental validation, the algorithm achieves 74.2% mAP and 64 FPS detection speed on Dior remote sensing dataset. experimental results show that the improved detection algorithm can effectively improve the detection capability of YOLOv5 for small and medium targets in remote sensing images and meet the real-time performance of detection.
DOI:10.1109/CCAI57533.2023.10201315