YOLO-based Object Detection Models: A Review and its ApplicationsObject detectionYOLOComputer VisionDeep LearningDatasetIn computer vision, object detection is the classical and most challenging problem to get accurate results in detecting objects. With the significant advancement of deep learning techniques...
We propose a YOLO-based semantic segmentation approach for optimizing dynamic noise in visual-inertial fused SLAM, addressing the challenge of high-precision real-time localization and mapping for low-speed scenarios in rescue mobile robotics. By leveraging the YOLO model, potential dynamic objects in...
改进YOLOv5在URPC2019上的实验结果 消融实验 提升 概述 这篇论文提出了一种基于改进 YOLOv5 的水下生物目标检测框架,以解决水下生物检测中的图像质量下降、复杂背景和不同尺度生物检测等问题,有效提高了水下目标检测的准确性和性能。 Highlight 1.引入了改进的YOLOv5目标检测框架:文章将实时目标检测模型(RTMDet)的...
Leveraging YOLOv8's state-of-the- art object detection capabilities, the approach enables rapid detection of damaged products and quality assessment, improving overall food quality control practices. In addition, YOLOv8 integration with cloud-based systems facilitates dynamic visualization and monitoring ...
To ensure the highest possible accuracy, we manually segmented each image from edge to edge, providing the YOLO model with detailed and accurate information for training. The dataset includes both training and testing sets, allowing for the evaluation of model performance and accuracy. Our dataset ...
In order to solve the problem of balanced detecting precision of traffic sign recognition model in different weather conditions, and it is difficult to detect occluded objects and small objects, this paper proposes a small object detection algorithm based on improved YOLOv5s in complex weather. ...
YOLOv5-l YOLOv5 640 12.1 35.31 67.30% a Measured in the COCO dataset. b How the authors of [12] assigned the new training strategy currently being developed based on knowledge distillation. The selection of an appropriate dataset is vital in Deep Learning model training. The advent of self-...
提出了一种基于YOLOv7改进的YOLO-ViT模型无人机红外车辆目标检测方法。文章首先介绍了红外车辆目标检测的挑战和现有方法的不足,然后提出了一种新的方法——YOLO-ViT,该方法采用轻量级MobileViT网络作为特征提取骨干网络,利用C3-PANet神经网络结构和CARAFE上采样方法等技术对目标进行多尺度检测,并通过优化锚盒大小来提高小...
YOLO-Based Efficient Vehicle Object Detection (mAP) of the proposed algorithm is up to 93.87%, which is 11.51%, 18.56% and 20.42% higher than that of YOLOv3, CornerNet, and Faster R-CNN, ... TN Liu,TN Liu,ZJ Zhu,... - 電腦學刊 被引量: 0发表: 2022年 Comparative Analysis on Deep...
Specifically, we compare and explore the applicability of various state-of-the-art object detectors trained on the BDD100K dataset, including YOLOv8, YOLOv7, YOLOv5, Scaled-YOLOv4, and YOLOR. Moreover, we investigate the deployment of these algorithms on edge devices, such as the NVIDIA ...