Enhancing YOLOv7 with GLF-Trans for Precision in Small Object Detection

Object detection techniques are essential for identifying objects and accurately determining their locations in images and videos. This paper proposes an approach to enhance the performance of YOLOv7, the YOLO (You Only Look Once) series, by improving detection accuracy, particularly for small objects. The accurate detection of small objects is crucial in various real-world applications such as autonomous driving, surveillance, and medical imaging, where the identification of small, often critical objects can have significant implications. The proposed method balances the current one-step and two-step algorithms used in object detection, with the goal of improving the accuracy of small object detection. We apply several improvements to the YOLOv7 framework, including the introduction of an attention module, the Global-Local Fusion Transformer (GLF-Trans), and the addition of a tiny-head. These enhancements are designed to further enhance the accuracy of small object detection. The experimental results show that the proposed method achieves higher accuracy than the previous model, with a specific improvement of 3.8% in <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mAP</sup>0.5 on the VisDrone2019 dataset.