A multi-scale surface target recognition algorithm based on attention fusion mechanism

Guo, Runze; Su, Shaojing; Zuo, Zhen; Sun, Bin

doi:10.1109/csaiee54046.2021.9543180

Cited by 1 publication

(2 citation statements)

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“…In order to verify the recognition effect of the model on surface targets, a dataset is established by visible light sensor acquisition and manual annotation [5]. As can be seen in Figure 10, the dataset contains different scenes and a total of different classes of surface targets such as fishing boats, yachts, buildings, bridge piers, and water drums.…”

Section: Methodsmentioning

confidence: 99%

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A Surface Target Recognition Algorithm Based on Coordinate Attention and Double-Layer Cascade

Guo

Zuo

et al. 2022

Wireless Communications and Mobile Computing

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As a branch of target recognition, surface target recognition plays an irreplaceable role in both military and civilian applications. However, the large target size variation, low image resolution, and high real-time requirements pose challenges to existing algorithms. To address the issues, we take YOLOv5 as a backbone and adopt coordinate attention and a double-layer cascade structure to enhance both the recognition performance and speed. Specifically, coordinate attention is introduced to guide the corresponding network to focus on discriminative features by capturing channel and location information. Meanwhile, the double-layer cascade structure is designed for finely extracting and aggregating semantic features and spatial features at different scales. We test the model on the COCO dataset, the VOC dataset, and self-built surface target dataset. Experimental results show that proposed coordinate attention module and multiscale module improve the recognition effect of multiscale surface targets and meet the requirement of real time.

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Section: Methodsmentioning

confidence: 99%

“…First, there are many types of surface targets with various postures. Their interclass differences are small and intraclass differences are large [5]. Simply extracting traditional features is no longer sufficient for practical needs.…”

Section: Introductionmentioning

confidence: 99%