Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (20): 236-244.DOI: 10.3778/j.issn.1002-8331.2106-0178

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Detection Method of Illegal Building Based on YOLOv5

YU Juan,LUO Shun   

  1. School of Economics and Management, Fuzhou University, Fuzhou 350108, China
  • Online:2021-10-15 Published:2021-10-21



  1. 福州大学 经济与管理学院,福州 350108


Aiming at solving the problem of slow detection rate and high false detection rate caused by the illegal buildings in the UAV images, which are mostly small targets and partially occluded targets, a detection method of illegal buildings based on YOLOv5 network is proposed. Firstly, at the beginning and end of original batch normalization module, centering and scaling calibration are added to enhance the effective features and form a more stable feature distribution, which strengthens the feature extraction ability of network model. Then, the smoothed KL(Kullback-Leibler) divergence loss function is adopted to replace the cross entropy in the confidence of original loss function, which further improves the generalization performance of model. Finally, the backbone feature extraction network of YOLOv5 is improved, and the residual module is replaced with the LSandGlass module to reduce information loss and eliminate low-resolution feature layers to reduce semantic loss. Experimental results show that the training of the proposed improved model is easier to make network converge in comparison with original YOLOv5, and the speed of detecting illegal buildings has been greatly improved, and then detection accuracy has been improved.

Key words: neural network, YOLOv5, illegal buildings detection, batch normalization, KL divergence



关键词: 神经网络, YOLOv5, 违章建筑检测, 批量标准化, KL散度