Abstract
To address the problem that a large number of small targets exist in remote-sensing images but are difficult to detect, in this paper, a DenseYOLOv5 detection model is proposed for practical applications. DenseYOLOv5 is based on YOLOv5s and the small target detection head P2 and its feature fusion part are added to improve the detection performance of small targets. To address the problem of semantic information loss of small targets due to continuous upsampling in YOLOv5s, DenseYOLOv5 reconstructs the feature fusion pyramid (FPN) structure and incorporates dense connections. In addition, DenseYOLOv5 also uses transposed convolution as the upsampling method to further improve the small target detection capability. DenseYOLOv5 can achieve better detection results with less memory and computational overhead and thus has better usability.
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