SenseLite: A YOLO-Based Lightweight Model for Small Object Detection in Aerial Imagery

Abstract

In the field of aerial remote sensing, detecting small objects in aerial images is challenging. Their subtle presence against broad backgrounds, combined with environmental complexities and low image resolution, complicates identification. While their detection is crucial for urban planning, traffic monitoring, and military reconnaissance, many deep learning approaches demand significant computational resources, hindering real-time applications. To elevate the accuracy of small object detection in aerial imagery and cater to real-time requirements, we introduce SenseLite, a lightweight and efficient model tailored for aerial image object detection. First, we innovatively structured the YOLOv5 model for a more streamlined structure. In the backbone, we replaced the original structure with cutting-edge lightweight neural operator Involution, enhancing contextual semantics and weight distribution. For the neck, we incorporated GSConv and slim-Neck, striking a balance between reduced computational complexity and performance, which is ideal for rapid predictions. Additionally, to enhance detection accuracy, we integrated a squeeze-and-excitation (SE) mechanism to amplify channel communication and improve detection accuracy. Finally, the Soft-NMS strategy was employed to manage overlapping targets, ensuring precise concurrent detections. Performance-wise, SenseLite reduces parameters by 30.5%, from 7.05 M to 4.9 M, as well as computational demands, with GFLOPs decreasing from 15.9 to 11.2. It surpasses the original YOLOv5, showing a 5.5% mAP0.5 improvement, 0.9% higher precision, and 1.4% better recall on the DOTA dataset. Compared to other leading methods, SenseLite stands out in terms of performance.