SimplestNet-Drone: An efficient and Accurate Object Detection Algorithm for Drone Aerial Image Analytics

Abstract

Images captured by drones are extremely difficult to detect due to varying camera angles, distances, sizes, and environmental conditions, making it challenging to accurately detect an object from a height. Nonetheless, object detection plays a crucial role in computer vision and has made significant improvements to images captured by drones. We apply the YOLOv5 framework with modified feature extraction and focus detection. The problem with aerial images is object size and viewing angle from a high altitude, so we proposed a single-stage object detection model called “SimplestNet-Drone”. We included a fourth prediction head to improve the object detection on the smallest objects and improve the detection speed. The algorithm's prediction accuracy is improved by adding an attention model mechanism, which detects attention regions in environments and suppresses unnecessary information. The model was trained and tested on the VisDorne dataset and compared with other object detection models. The model shows great improvement, with a mean average precision of 63.72%, and has improved the Yolo architecture. A real-time implementation of our model can be watched in the following YouTube video: https://youtu.be/De8t4tjtb6w