Can I read Low-altitude Small-sized Object Detection Using Lightweight Feature-enhanced Convolutional Neural Network on EtoBox?

Low-altitude Small-sized Object Detection Using Lightweight Feature-enhanced Convolutional Neural Network by Ye Tao; Zhao Zongyang; Zhang Jun; Chai Xinghua; Zhou Fuqiang is a Engineering article available to read on EtoBox.

What is Low-altitude Small-sized Object Detection Using Lightweight Feature-enhanced Convolutional Neural Network about?

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

Who reads Low-altitude Small-sized Object Detection Using Lightweight Feature-enhanced Convolutional Neural Network?

Author

Ye Tao; Zhao Zongyang; Zhang Jun; Chai Xinghua; Zhou Fuqiang

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2021

Language

EN

Field

Engineering (Physical Sciences)