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• W3210079728 abstract "Since small objects occupy less pixels in the image and are difficult to recognize. Small object detection has always been a research difficulty in the field of computer vision. Aiming at the problems of low sensitivity and poor detection performance of YOLOv3 for small objects. AFYOLO, which is more sensitive to small objects detection was proposed in this paper. Firstly, the DenseNet module is introduced into the low-level layers of backbone to enhance the transmission ability of objects information. At the same time, a new mechanism combining channel attention and spatial attention is introduced to improve the feature extraction ability of the backbone. Secondly, a new feature pyramid network (FPN) is proposed to better obtain the features of small objects. Finally, ablation studies on ImageNet classification task and MS-COCO object detection task verify the effectiveness of the proposed attention module and FPN. The results on Wider Face datasets show that the AP of the proposed method is 11.89%higher than that of YOLOv3 and 8.59%higher than that of YOLOv4. All of results show that AFYOLO has better ability for small object detection." @default.
• W3210079728 created "2021-11-08" @default.
• W3210079728 creator A5012506279 @default.
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• W3210079728 date "2022-03-04" @default.
• W3210079728 modified "2023-09-24" @default.
• W3210079728 title "Small object detection combining attention mechanism and a novel FPN" @default.
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• W3210079728 doi "https://doi.org/10.3233/jifs-211905" @default.
• W3210079728 hasPublicationYear "2022" @default.
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