Improving Tiny Object Detection in Aerial Images with Yolov5 - Sharba, Ahmed; Kanaan, Hussain (Journal of Engineering and Sustainable Development, 2025)
Object detection is a major area of computer vision work, particularly for aerial surveillance and traffic control applications, where detecting vehicles from aerial images is essential. However, such images often lack semantic detail and struggle to identify small, densely packed objects accurately. This paper proposes improvements to the You Only Look Once version 5 (YOLOv5) model to enhance small object detection. Key modifications include adding a new prediction head with a 160×160 feature map, replacing the Sigmoid Linear Unit (SiLU) activation function with the Exponential Linear Unit (ELU), and swapping the Spatial Pyramid Pooling – Fast (SPPF) module with the Spatial Pyramid Pooling (SPP) module. The enhanced model was tested on two datasets: Dataset for Object Detection in Aerial Images (DOTA) v1.5 and CarJet, which focused on vehicle and plane detection. Results showed a 7.1% increase in mean Average Precision (mAP) on the DOTA dataset and a 2.3% improvement on the CarJet dataset, measured with an Intersection over Union (IoU) threshold of 0.5. These architectural changes to YOLOv5 notably improve small object detection accuracy, offering valuable potential for aerial surveillance and traffic control tasks.