Low-altitude small-sized object detection using lightweight feature-enhanced convolutional neural network

Abstract

Unauthorized operations referred to as "black flights" of unmanned aerial vehicles (UAVs) pose a significant danger to public safety, and existing low-attitude object detection algorithms encounter difficulties in balancing detection precision and speed. Additionally, their accuracy is insufficient, particularly for small objects in complex environments. To solve these problems, we propose a lightweight feature-enhanced convolutional neural network able to perform detection with high precision detection for low-attitude flying objects in real time to provide guidance information to suppress black-flying UAVs. The proposed network consists of three modules. A lightweight and stable feature extraction module is used to reduce the computational load and stably extract more low-level feature, an enhanced feature processing module significantly improves the feature extraction ability of the model, and an accurate detection module integrates low-level and advanced features to improve the multiscale detection accuracy in complex environments, particularly for small objects. The proposed method achieves a detection speed of 147 frames per second (FPS) and a mean average precision (mAP) of 90.97% for a dataset composed of flying objects, indicating its potential for low-altitude object detection. Furthermore, evaluation results based on microsoft common objects in context (MS COCO) indicate that the proposed method is also applicable to object detection in general.