Improved YOLOv5 Network with CBAM for Object Detection Vision Drone

Jinsu An, Muhamad Dwisnanto Putro, Adri Priadana, Kang-Hyun Jo

Abstract: Recently, drones have been applied and used in various tasks in many fields. Due to the development of camera technology and hardware, it is possible to detect objects using deep learning technology in real time. Most of the object sizes of videos and images taken by drones are very small objects. The dataset that collects these image data is the VisDrone dataset. As deep learning technology develops a lot, object detection accuracy is getting higher and higher. However, it is still hard to perform object detection in real-time and show high object detection accuracy. In this paper, we combine YOLOv5 and CBAM to increase object detection accuracy by focusing more on the features required for object detection. The model is trained using the VisDrone dataset, and the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{mAP}$</tex> value is measured at 22. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$56\text{mAP}$</tex> ,which is 2. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$45\text{mAP}$</tex> higher than the original YOLOv5.