2022
DOI: 10.1155/2022/8703380
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Gated Channel Attention Mechanism YOLOv3 Network for Small Target Detection

Abstract: In order to solve the problem of low recognition rate and high missed rate in current target detection task, this paper proposes an improved YOLOv3 algorithm based on a gated channel attention mechanism (GCAM) and adaptive up-sampling module. Firstly, darknet-53 is used as the backbone network to extract image basic features. Secondly, an adaptive up-sampling module is introduced to expand the low-resolution convolutional feature images, which effectively enhances the fusion efficiency of the convolutional fea… Show more

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Cited by 1 publication
(1 citation statement)
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“…Tong et al [65] introduced a channel attention-based DenseNet network that rapidly and accurately captures key features from images, thus improving the classification of remote sensing image scenes. To tackle the issue of low recognition rates and high miss rates in current object detection tasks, Yang et al [66] proposed an improved YOLOv3 algorithm incorporating a gated channel attention mechanism (GCAM) and an adaptive upsampling module. Results showed that the improved approach adapts to multi-scale object detection tasks in complex scenes and reduces the omission rate of small objects.…”
Section: Gated Channel Attention Mechanismmentioning
confidence: 99%
“…Tong et al [65] introduced a channel attention-based DenseNet network that rapidly and accurately captures key features from images, thus improving the classification of remote sensing image scenes. To tackle the issue of low recognition rates and high miss rates in current object detection tasks, Yang et al [66] proposed an improved YOLOv3 algorithm incorporating a gated channel attention mechanism (GCAM) and an adaptive upsampling module. Results showed that the improved approach adapts to multi-scale object detection tasks in complex scenes and reduces the omission rate of small objects.…”
Section: Gated Channel Attention Mechanismmentioning
confidence: 99%