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 tested on the basis of real, multimodal ...
et al. YOLOv5 Drone Detection Using Multimodal Data Registered by the Vicon System. Sensors 23 (2023). Hartley, R. & Zisserman, A. Multiple view geometry in computer vision (Cambridge university press, 2003). Krzeszowski, T., Switonski, A., Zielinski, M., Wojciechowski, K. & Rosner,...
TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios Xingkui Zhu1 * Shuchang Lyu1 * Xu Wang 1 Qi Zhao1 † 1 Beihang University, Beijing, China {adlith, lyushuchang, sy2002406, zhaoqi}...
git clone https://github.com/chengshuxiao/YOLOv5-ODConvNeXt.git # clone cd YOLOv5-ODConvNeXt pip install -r requirements.txt # install Inference with detect.py A trained YOLOv5-ODConvNeXt model is provided in checkpoints/yolov5-odconvnext.pt,the detection results are saved in runs/detect py...
The pipeline included the following steps: (a) aerial data collection and data preprocessing (3D reconstruction by photogrammetry), (b) low labor-cost broccoli position detection using YOLO v5 at the seedling stage, (c) low labor-cost broccoli head segmentation using pretrained BiSeNet v2 on the...
Detecting small objects in complex scenes, such as those captured by drones, is a daunting challenge due to the difficulty in capturing the complex features of small targets. While the YOLO family has achieved great success in large target detection, its performance is less than satisfactory when...
In this drone camera detection model, YOLOv5l architecture is used for large models with 46.5 million parameters, an accuracy of 67.3, and a 10.1 ms GPU time. The YOLOv5 networks can detect objects using the S × S grid cell concept with a value of S = 7. The grids determine whether...
The YOLOv7 architecture is integrated with the backbone network, neck component, feature pyramid and detection heads to detect and estimate the depth of an apple. It will detect and estimate the apple depth by using labels of drone images and will also allow for specific orchard management and...
Damaged building detection model with YOLOv10 on Rescuenet dataset object-detectiondisaster-managementyolov5drone-imagesyolov8yolov10 UpdatedMar 26, 2025 Jupyter Notebook mr-ravin/Aerial-Semantic-Segmentation-Drone-using-Pytorch Star1 Code Issues ...
Object Detection Drone vs Bird OBSS YOLOv5+Track Boosting Papers PaperCodeResultsDateStars Track Boosting and Synthetic Data Aided Drone Detection 24 Nov 2021 Previous 1 Next Showing 1 to 1 of 1 papers Dataset Loaders Edit mili4302/BirdDroneDataset 0 Tasks Edit Object Detection Usage...