Journal of Jilin University(Engineering and Technology Edition) ›› 2025, Vol. 55 ›› Issue (5): 1742-1748.doi: 10.13229/j.cnki.jdxbgxb.20240459

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Improved YOLOv5s algorithm for target detection in hyperspectral remote sensing images

Li TIAN(),Yu-hui JIA   

  1. College of Information and Electrical Engineering，Heilongjiang Bayi Agricultural University，Daqing 163319，China
  • Received:2024-04-28 Online:2025-05-01 Published:2025-07-18

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Abstract:

The spectral resolution of hyperspectral images is very high， and there are many bands of ground objects， so the spectral difference between the target and the background is very small， which is easy to cause spectral confusion， and the accuracy of target detection is low. Therefore， an image object detection method based on improved YOLOv5s algorithm is proposed. A feature pyramid is established and multi-scale weighting is implemented. The weights between different layers in the feature pyramid are used to weight and fuse the features and introduce them into the attention mechanism. The spectral features of the spatial attention mechanism are output， and the feature value is used as a comparison reference. The hyperspectral image features obtained after calibration are used as the input of the improved YOLOv5s algorithm to effectively distinguish the tiny spectral feature differences in the image， avoid spectral confusion， calculate the overlap area between the detection frame and the real frame according to the central value， complete the target detection， and ensure the detection accuracy. Experiments show that the proposed method has a high accuracy for detecting ground objects in hyperspectral remote sensing images. When detecting 1 057 p pixel images， the frame rate is as high as 60fps， and the comprehensive performance is excellent.

Key words: improving YOLOv5s algorithm, hyperspectral remote sensing images, spatial attention, object detection

CLC Number: 

  • TP123

Fig.1

Multi-scale weighted bidirectional image feature fusion diagram"

Fig.2

Original hyperspectral remote sensing image"

Fig.3

Comparison results of target detection in hyperspectral remote sensing images"

Table 1

Comparison of results based on detection frame rate"

像素大小/p检测帧率/fps
特征增强法增强小目标特征法改进卷积神经网络法

方法

本文

15748.660.660.0120.2
25748.460.848.0120.8
35748.260.248.8120.6
45748.048.248.6120.0
55724.848.448.6100.0
65724.448.248.4100.0
75724.248.048.2100.0
85724.248.048.280.0
95724.048.048.080.0
1 05722.848.248.060.0
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