Gated Channel Attention Mechanism YOLOv3 Network for Small Target Detection

Yang, Xi; Shi, Jin; Zhang, Juan

doi:10.1155/2022/8703380

Cited by 1 publication

(1 citation statement)

References 24 publications

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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%

WAID: A Large-Scale Dataset for Wildlife Detection with Drones

Mou,

Liu,

Zhu

et al. 2023

Applied Sciences

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Drones are widely used for wildlife monitoring. Deep learning algorithms are key to the success of monitoring wildlife with drones, although they face the problem of detecting small targets. To solve this problem, we have introduced the SE-YOLO model, which incorporates a channel self-attention mechanism into the advanced real-time object detection algorithm YOLOv7, enabling the model to perform effectively on small targets. However, there is another barrier; the lack of publicly available UAV wildlife aerial datasets hampers research on UAV wildlife monitoring algorithms. To fill this gap, we present a large-scale, multi-class, high-quality dataset called WAID (Wildlife Aerial Images from Drone), which contains 14,375 UAV aerial images from different environmental conditions, covering six wildlife species and multiple habitat types. We conducted a statistical analysis experiment, an algorithm detection comparison experiment, and a dataset generalization experiment. The statistical analysis experiment demonstrated the dataset characteristics both quantitatively and intuitively. The comparison and generalization experiments compared different types of advanced algorithms as well as the SE-YOLO method from the perspective of the practical application of UAVs for wildlife monitoring. The experimental results show that WAID is suitable for the study of wildlife monitoring algorithms for UAVs, and SE-YOLO is the most effective in this scenario, with a mAP of up to 0.983. This study brings new methods, data, and inspiration to the field of wildlife monitoring by UAVs.

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Section: Gated Channel Attention Mechanismmentioning

confidence: 99%