Abstract
Infrared imaging technology has a wide range of applications across various fields, with one of its most critical uses being the detection of small infrared targets. However, model-driven approaches often lack robustness in identifying these small targets, while current deep learning-based methods face challenges in effectively extracting and integrating features. Additionally, appropriate labeling strategies for small infrared targets remain underdeveloped. To address these limitations, this paper proposes a novel detection method based on YOLOv7. Specifically, an attention module leveraging Depthwise Convolution is incorporated into the backbone of YOLOv7. Furthermore, a new Feature Fusion Neck is designed to replace the original neck component of YOLOv7. Lastly, a novel label assignment strategy is introduced. The proposed method achieves a mAP@0.5 of 99.5% and a mAP@0.75 of 71.6% on a public dataset, surpassing the baseline YOLOv7 by 1% and 4.6%, respectively. Compared to state-of-the-art deep learning object detection methods, the proposed approach demonstrates superior performance.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call