Drone-Yolo在无人机数据集上取得了巨大的成功,mAP0.5指标上取得了显著改进,在VisDrone2019-test上增加了13.4%,在VisDrone2019-val上增加了17.40%。这篇文章我首先复现Drone-Yolo,然后,在Drone-Yolo的基础上加入我自己对小目标检测的改进。 YoloV5改进策略:独家原创,全网首发,复现Drone-Yolo,以及改进方法-CSDN博客...
drone localizationThis work is focused on the preliminary stage of the 3D drone tracking challenge, namely the precise detection of drones on images obtained from a synchronized multi-camera system. The YOLOv5 deep network with different input resolutions is trained and tested on the basis ...
Based on YOLOv5, we integrate the convolution block attention model ( CBAM ) and the attention mechanism of Swin Transformer to enable it to effectively focus on the attention area in dense small object scene and reduce the calculation amount. We also adopt the BiFPN structure for the structure...
Our architecture integrates YoloV5 with a gradient boosting classifier; the rationale is to use a scalable and efficient object recognition system coupled with a classifier that is able to incorporate samples of variable difficulty. In an ablation study, we test different architectures of YoloV5 and...
YOLOv5drone imageRaspberry Pi 4Recently, drones are used in all fields. The video captured by this drone is sent to the terminal for analysis. In terms of speed, performance, and latency, it would be an advantage if the analysis of the image or video is done onboard, the drone, and ...
This study introduces a real-time vehicle detection framework leveraging the you only look once (YOLO) algorithm for precise identification of vehicles, employing the camera on the DJI Tello drone. The research is underpinned by a rich dataset encompassing approximate...
In TPH-YOLOv5++, cross-layer asymmetric transformer (CA-Trans) is designed to replace the additional prediction head while maintain the knowledge of this head. By using a sparse local attention (SLA) module, the asymmetric information between the additional head and other heads can be captured ...
Consequently, conventional object detection algorithms are often unsuitable for direct application in drone scenarios. To address these challenges, this study proposes a drone object detection algorithm model based on YOLOv5, named SMT-YOLOv5 (Small Target-YOLOv5). The enhancement strategy involves ...
YOLOv5; autonomous drone detection; image recognition; machine learning; mAP; unmanned aerial vehicle (UAV)1. Introduction Drones are becoming increasingly popular. Most are inexpensive, flexible, and lightweight [1]. They are utilized in a variety of industries, including the military, construction...
This work is focused on the preliminary stage of the 3D drone tracking challenge, namely the precise detection of drones on images obtained from a synchronized multi-camera system. The YOLOv5 deep network with different input resolutions is trained and t