Small target detection with remote sensing images based on an improved YOLOv5 algorithm

Abstract

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IntroductionSmall target detection with remote sensing images is a challenging topic due to the small size of the targets, complex, and fuzzy backgrounds.MethodsIn this study, a new detection algorithm is proposed based on the YOLOv5s algorithm for small target detection. The data enhancement strategy based on the mosaic operation is applied to expand the remote image training sets so as to diversify the datasets. First, the lightweight and stable feature extraction module (LSM) and C3 modules are combined to form the feature extraction module, called as LCB module, to extract more features in the remote sensing images. Multi-scale feature fusion is realized based on the Res 2 unit, Dres 2, and Spatial Pyramid Pooling Small (SPPS) models, so that the receptive field can be increased to obtain more multi-scale global information based on Dres2 and retain the obtained feature information of the small targets accordingly. Furthermore, the input size and output size of the network are increased and set in different scales considering the relatively less target features in the remote images. Besides, the Efficient Intersection over Union (EIoU) loss is used as the loss function to increase the training convergence velocity of the model and improve the accurate regression of the model.Results and discussionThe DIOR-VAS and Visdrone2019 datasets are selected in the experiments, while the ablation and comparison experiments are performed with five popular target detection algorithms to verify the effectiveness of the proposed small target detection method.

Keywords

• small target detection
• remote sensing images
• YOLOv5s
• deep learning
• EIoU loss