In this work, we present and evaluate a method to perform real-time multiple drone detection and three-dimensional localization using state-of-the-art tiny-YOLOv4 object detection algorithm and stereo triangulation. Our computer vision approach eliminates the need for computationally expensive stereo matching algorithms, thereby significantly reducing the memory footprint and making it deployable on embedded systems. Our drone detection system is highly modular (with support for various detection algorithms) and capable of identifying multiple drones in a system, with real-time detection accuracy of up to 77\% with an average FPS of 332 (on Nvidia Titan Xp). We also test the complete pipeline in AirSim environment, detecting drones at a maximum distance of 8 meters, with a mean error of $23\%$ of the distance. We also release the source code for the project, with pre-trained models and the curated synthetic stereo dataset.
Object detection on drone-captured scenarios is a recent popular task. As drones always navigate in different altitudes, the object scale varies violently, which burdens the optimization of networks. Moreover, high-speed and low-altitude flight bring in the motion blur on the densely packed objects, which leads to great challenge of object distinction. To solve the two issues mentioned above, we propose TPH-YOLOv5. Based on YOLOv5, we add one more prediction head to detect different-scale objects. Then we replace the original prediction heads with Transformer Prediction Heads (TPH) to explore …
Following the recent advances in deep networks, object detection and tracking algorithms with deep learning backbones have been improved significantly; however, this rapid development resulted in the necessity of large amounts of annotated labels. Even if the details of such semi-automatic annotation processes for most of these datasets are not known precisely, especially for the video annotations, some automated labeling processes are usually employed. Unfortunately, such approaches might result with erroneous annotations. In this work, different types of annotation errors for object detectio…
Performance Optimization of YOLO-FEDER FusionNet for Robust Drone Detection in Visually Complex Environments
Institute for the Protection of Terrestrial Infrastructures, German Aerospace Center（地面基础设施保护研究所，德国航天中心）；ACIDA Lab, University of Applied Sciences Darmstadt（达姆施塔特应用科学大学ACIDA实验室）；European University of Technology（欧洲技术大学）
National Central University（中央大学）；National Chiao Tung University（交通大学）
The development of lightweight object detectors is essential due to the limited computation resources. To reduce the computation cost, how to generate redundant features plays a significant role. This paper proposes a new lightweight Convolution method Cross-Stage Lightweight (CSL) Module, to generate redundant features from cheap operations. In the intermediate expansion stage, we replaced Pointwise Convolution with Depthwise Convolution to produce candidate features. The proposed CSL-Module can reduce the computation cost significantly. Experiments conducted at MS-COCO show that the proposed…
NHL Stenden University of Applied Sciences（NHL斯坦德应用科学大学）；NHL Stenden Center of Expertise in Computer Vision & Data Science（NHL斯坦德计算机视觉与数据科学专业知识中心）
With the introduction of new regulations in the European Union, the future of Beyond Visual Line Of Sight (BVLOS) drones is set to bloom. This led to the creation of the theBEAST project, which aims to create an autonomous security drone, with focus on those regulations and on safety. This technical paper describes the first steps of a module within this project, which revolves around detecting obstacles so they can be avoided in a fail-safe landing. A deep learning powered object detection method is the subject of our research, and various experiments are held to maximize its performance, suc…
School of Mechanical Engineering, Hubei University of Technology（湖北工业大学机械工程学院）
Fire-detection technology is of great importance for successful fire-prevention measures. Image-based fire detection is one effective method. At present, object-detection algorithms are deficient in performing detection speed and accuracy tasks when they are applied in complex fire scenarios. In this study, a lightweight fire-detection algorithm, Light-YOLOv5 (You Only Look Once version five), is presented. First, a separable vision transformer (SepViT) block is used to replace several C3 modules in the final layer of a backbone network to enhance both the contact of the backbone network to gl…
LTV-YOLO: A Lightweight Thermal Object Detector for Young Pedestrians in Adverse Conditions
Automotive and Surface Transportation National Research Council Canada（交通运输国家研究院加拿大）；Department of Civil and Environmental Engineering University of Western Ontario（土木与环境工程系温哥华大学）
LAF-YOLOv10 with Partial Convolution Backbone, Attention-Guided Feature Pyramid, Auxiliary P2 Head, and Wise-IoU Loss for Small Object Detection in Drone Aerial Imagery
Department of Information Technology（信息科技系）；Department of Computer Science and Engineering（计算机科学与工程系）
Now a days, UAVs such as drones are greatly used for various purposes like that of capturing and target detection from ariel imagery etc. Easy access of these small ariel vehicles to public can cause serious security threats. For instance, critical places may be monitored by spies blended in public using drones. Study in hand proposes an improved and efficient Deep Learning based autonomous system which can detect and track very small drones with great precision. The proposed system consists of a custom deep learning model Tiny YOLOv3, one of the flavors of very fast object detection model You…
Adaptive Enhancement and Dual-Pooling Sequential Attention for Lightweight Underwater Object Detection with YOLOv10
1,2Department of Computer Science；Engineering, Dhaka University of Engineering \& Technology Gazipur, 1707, Bangladesh 3Department of Computer Science；Engineering, BRAC University Dhaka, Bangladesh
This is the paper for the first place winning solution of the Drone vs. Bird Challenge, organized by AVSS 2021. As the usage of drones increases with lowered costs and improved drone technology, drone detection emerges as a vital object detection task. However, detecting distant drones under unfavorable conditions, namely weak contrast, long-range, low visibility, requires effective algorithms. Our method approaches the drone detection problem by fine-tuning a YOLOv5 model with real and synthetically generated data using a Kalman-based object tracker to boost detection confidence. Our results …
Northwest Minzu University（西北民族大学）；China Mobile Design Institute Co., Ltd.（中国移动设计院有限公司）
Small object detection has important application value in the fields of autonomous driving and drone scene analysis. As one of the most advanced object detection algorithms, YOLOv3 suffers some challenges when detecting small objects, such as the problem of detection failure of small objects and occluded objects. To solve these problems, an improved YOLOv3 algorithm for small object detection is proposed. In the proposed method, the dilated convolutions mish (DCM) module is introduced into the backbone network of YOLOv3 to improve the feature expression ability by fusing the feature maps of di…
CFIS-YOLO: A Lightweight Multi-Scale Fusion Network for Edge-Deployable Wood Defect Detection
School of Information and Engineering, Minzu University of China（中央民族大学信息工程学院）；School of Computer and Information Technology, Beijing Jiaotong University（北京交通大学计算机与信息技术学院）；School of Information and Communication Engineering, Beijing University of Posts and Telecommunications（北京邮电大学信息与通信工程学院）；Faculty of Science, The University of Hong Kong（香港大学理学院）
SBP-YOLO:A Lightweight Real-Time Model for Detecting Speed Bumps and Potholes toward Intelligent Vehicle Suspension Systems
Key Laboratory for Optoelectronic Technology and Systems, Ministry of Education, College of Optoelectronic Engineering, Chongqing University（光电技术及系统重点实验室、教育部长春电子工程学院、重庆大学）
YOLOBirDrone: Dataset for Bird vs Drone Detection and Classification and a YOLO based enhanced learning architecture
Department of Intelligent Machine and Computing System, CSIR CSIO（智能机器与计算系统部门、CSIR CSIO）；School of Computing and Electrical Engineering, IIT Mandi（计算与电子工程学院、IIT Mandi）
Fostc3net:A Lightweight YOLOv5 Based On the Network Structure Optimization
Stevens Institute of Technology（史蒂文斯理工学院）；Huacong Qingjiao Information Technology Co., Ltd（华聪青教信息技术有限公司）；San Francisco Bay University（旧金山湾大学）；Universidad Tecnológico Universitam；University of Maine at Presque Isle（缅因大学普雷斯克艾尔分校）
YOLO-PVC: 2D-to-3D Consolidation of Slice-wise Detections for Volumetric Liver Tumor Localization in MRI
ESME Research Lab（ESME研究实验室）；Université Paris-Est Créteil（巴黎东部克雷泰伊大学）；LRE EPITA（EPITA LRE实验室）；Institute of Space Technology（空间技术研究所）；Henri Mondor University Hospital（亨利·蒙多大学医院）
