Traffic Sign Recognition Application Using Yolov5 Architecture

Snegireva, Daria; Perkova, Anastasiia

doi:10.1109/rusautocon52004.2021.9537355

Cited by 20 publications

(6 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, the features map is introduced to the neck which in turn detects the bounding boxes. Finally, the head gives the detection results (class, confidence score, location(s) and size(s) of the bounding box(es)) [ 31 ]. Another advantage is its ability to enhance the training data, where the data loader of YOLOv5, for example, applies three types of data enhancement: color space adjustment, scaling and mosaic enhancement [ 30 ].…”

Section: Methodsmentioning

confidence: 99%

Eye Recognition by YOLO for Inner Canthus Temperature Detection in the Elderly Using a Transfer Learning Approach

Ghourabi

Mourad-Chehade

Chkeir

2023

Sensors

View full text Add to dashboard Cite

Early detection of physical frailty and infectious diseases in seniors is important to avoid any fatal drawback and promptly provide them with the necessary healthcare. One of the major symptoms of viral infections is elevated body temperature. In this work, preparation and implementation of multi-age thermal faces dataset is done to train different “You Only Look Once” (YOLO) object detection models (YOLOv5,6 and 7) for eye detection. Eye detection allows scanning for the most accurate temperature in the face, which is the inner canthus temperature. An approach using an elderly thermal dataset is performed in order to produce an eye detection model specifically for elderly people. An application of transfer learning is applied from a multi-age YOLOv7 model to an elderly YOLOv7 model. The comparison of speed, accuracy, and size between the trained models shows that the YOLOv7 model performed the best (Mean average precision at Intersection over Union of 0.5 (mAP@.5) = 0.996 and Frames per Seconds (FPS) = 150). The bounding box of eyes is scanned for the highest temperature, resulting in a normalized error distance of 0.03. This work presents a fast and reliable temperature detection model generated using non-contact infrared camera and a deep learning approach.

show abstract

Section: Methodsmentioning

confidence: 99%

Eye Recognition by YOLO for Inner Canthus Temperature Detection in the Elderly Using a Transfer Learning Approach

Ghourabi

Mourad-Chehade

Chkeir

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…By default, the network is passed an input image with dimensions 640 × 640 × 3. The CBL module consists of a convolution, a batch normalization, and an activation function based on ReLU [24].…”

Section: Yolomentioning

confidence: 99%

Design and Implementation of a Camera-Based Tracking System for MAV Using Deep Learning Algorithms

Hensel,

Marinov,

Panter

2023

Computation

View full text Add to dashboard Cite

In recent years, the advancement of micro-aerial vehicles has been rapid, leading to their widespread utilization across various domains due to their adaptability and efficiency. This research paper focuses on the development of a camera-based tracking system specifically designed for low-cost drones. The primary objective of this study is to build up a system capable of detecting objects and locating them on a map in real time. Detection and positioning are achieved solely through the utilization of the drone’s camera and sensors. To accomplish this goal, several deep learning algorithms are assessed and adopted because of their suitability with the system. Object detection is based upon a single-shot detector architecture chosen for maximum computation speed, and the tracking is based upon the combination of deep neural-network-based features combined with an efficient sorting strategy. Subsequently, the developed system is evaluated using diverse metrics to determine its performance for detection and tracking. To further validate the approach, the system is employed in the real world to show its possible deployment. For this, two distinct scenarios were chosen to adjust the algorithms and system setup: a search and rescue scenario with user interaction and precise geolocalization of missing objects, and a livestock control scenario, showing the capability of surveying individual members and keeping track of number and area. The results demonstrate that the system is capable of operating in real time, and the evaluation verifies that the implemented system enables precise and reliable determination of detected object positions. The ablation studies prove that object identification through small variations in phenotypes is feasible with our approach.

show abstract

“…YOLO is one of the most famous object detection algorithms due to its speed and accuracy. Many works related to car traffic have used YOLOv5 to detect traffic signals [47,48], obstacles [49], traffic flow [50], or to classify vehicles [51].…”

Section: Detection Of the Crash Time In The Crash Videomentioning

confidence: 99%

Who Was Wrong? An Object Detection Based Responsibility Assessment System for Crossroad Vehicle Collisions

Yawovi

Kikuchi²,

Ozono³

2022

View full text Add to dashboard Cite

Car crashes, known also as vehicle collisions, are recurrent events that occur every day. As long as vehicles exist, vehicle collisions will, unfortunately, continue to occur. When a car crash occurs, it is important to investigate and determine the actors’ responsibilities. The police in charge of that task, as well as claims adjusters, usually process manually by going to the crash spot, collecting data on the field, drafting the crash, and assessing responsibilities based on road rules. This later task of assessing responsibilities usually takes time and requires enough knowledge of road rules. With the aim to support the police and claims adjusters and simplify the process of responsibility determination, we built a system that can automatically assess actors’ responsibilities within a crossroad crash. The system is mainly based on image detection and uses a rule-based knowledge system to assess responsibilities within driving recorders’ videos. It uses the crash video recorded by one of the involved vehicles’ driving recorders as the input data source and outputs the evaluation of each actor’s responsibility within the crash. The rule-based knowledge system was implemented to make the reasoning about responsibility assessment clear and allow users to easily understand the reasons for the results. The system consists of three modules: (a) a crash time detection module, (b) a traffic sign detection module, and (c) a responsibility assessment module. To detect a crash within a video, we discovered that the simple application of existing vehicle detection models would result in wrong detections with many false positives. To solve the issue, we made our proposed model take into account only the collided vehicle, its shape, and its position within the video. Moreover, with the biggest challenge being finding data to train the system’s detection models, we built our own dataset from scratch with more than 1500 images of head-on car crashes within the context of crossroad accidents taken by the driving recorder of one of the vehicles involved in the crash. The experiment’s results show how the system performs in (1) detecting the crash time, (2) detecting traffic signs, and (3) assessing each party’s responsibility. The system performs well when light conditions and the visibility of collided objects are good and traffic lights’ view distances are close.

show abstract

Traffic Sign Recognition Application Using Yolov5 Architecture

Cited by 20 publications

References 7 publications

Eye Recognition by YOLO for Inner Canthus Temperature Detection in the Elderly Using a Transfer Learning Approach

Eye Recognition by YOLO for Inner Canthus Temperature Detection in the Elderly Using a Transfer Learning Approach

Design and Implementation of a Camera-Based Tracking System for MAV Using Deep Learning Algorithms

Who Was Wrong? An Object Detection Based Responsibility Assessment System for Crossroad Vehicle Collisions

Contact Info

Product

Resources

About