ISSN 1000-1239 CN 11-1777/TP

### A Lightweight UAV Object Detection Algorithm Based on Iterative Sparse Training

Hou Xin1, Qu Guoyuan2, Wei Dazhou2, Zhang Jiacheng3

1. 1(Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190);2(Chinese Aeronautical Radio Electronics Research Institute, Shanghai 200241);3(School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876)
• Online:2022-04-01
• Supported by:
This work was supported by the National Key Basic Research and Development Program of China (2018YFC0809300, 2107YFB0202105, 2016YFB0200803, 2017YFB0202302), the National Natural Science Foundation of China (61972376), and the Beijing Natural Science Foundation (L182053).

Abstract: With the maturity of UAV (unmanned aerial vehicle) technology, vehicles equipped with cameras are widely used in various fields, such as security and surveillance, aerial photography and infrastructure inspection. It is important to automatically and efficiently analyze and understand the visual data collected from vehicles. The object detection algorithm based on deep convolutional neural network has made amazing achievements in many practical applications, but it is often accompanied by great resource consumption and memory occupation. Thus, it is challenging to run deep convolutional neural networks directly on embedded devices with limited computing power carried by vehicles, which leads to high latency. In order to meet these challenges, a novel pruning algorithm based on iterative sparse training is proposed to improve the computational effectiveness of the classic object detection network YOLOv3 (you only look once). At the same time, different data enhancement methods and related optimization means are combined to ensure that the precision error of the detector before and after compression is within an acceptable range. Experimental results indicate that the pruning scheme based on iterative sparse training proposed in this paper achieves a considerable compression rate of YOLOv3 within slightly decline in precision. The original YOLOv3 model contains 61.57 MB weights and requires 139.77GFLOPS(floating-point operations). With 98.72% weights and 90.03% FLOPS reduced, our model still maintains a decent accuracy, with only 2.0% mAP(mean average precision) loss, which provides support for real-time application of UAV object detection.

CLC Number: