Small target detection algorithm based on multi-branch stacking and new sampling transition module
Qingyao Lin, Rugang Wang, Yuanyuan Wang, Feng Zhou, Narendra Khatri, Narendra Khatri, Narendra Khatri, Narendra Khatri
TL;DR
This paper introduces AMT-SSD, a new algorithm that improves small target detection by using multi-branch stacking and an attention-based sampling module.
Contribution
The novel AMT-SSD algorithm combines multi-branch stacking and an attention-based sampling transition module to enhance small target detection.
Findings
AMT-SSD achieves 84.6% mAP on the PASCAL VOC dataset.
The algorithm reaches 53.4% mAP on the MS COCO dataset.
The new modules effectively reduce feature loss during detection.
Abstract
Aiming at the problem that the SSD algorithm does not fully extract the feature information contained in each feature layer, as well as the feature information is easily lost during the sampling process, which makes the feature expression ineffective and leads to insufficient performance in small target detection. In this paper, AMT-SSD is proposed, a small target detection algorithm that incorporates the multi-branch stacking and new sampling transition module of the attention mechanism. In this algorithm, the composite attention mechanism is utilized to improve the correlation of features of the samples to be detected in terms of spatial and channels, and the efficiency of the algorithm; secondly, multi-branch stacking module is used to extract multi-size features for each feature layer, and different sizes of convolution kernels are utilized in parallel to fully extract their…
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Taxonomy
TopicsAdvanced Neural Network Applications · Advanced Image and Video Retrieval Techniques · Industrial Vision Systems and Defect Detection