scholarly journals Detection of Traffic Light using Machine Vision for Autonomous Vehicles Application

2019 ◽  
Vol 9 (2) ◽  
pp. 1033-1037
Keyword(s):  
Image Processing ◽  
Urban Areas ◽  
Detection System ◽  
Autonomous Vehicle ◽  
Weather Conditions ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Traffic Light ◽  
Light Detection ◽  
Traffic Lights

Traffic light detection is crucial to decrease the traffic light accidents at intersections and to realize autonomous driving. There are so many existing methods to detect traffic light. However, these approaches have several limitations, such as not function well in complex driving environments. Hence, to overcome such constraints, the traffic light detection for the autonomous vehicle using image processing technique is proposed. The experiments are carried out using 114 scene images that consist of 209 traffic lights with different angles, weather conditions, and distance. An image processing technique, Hough Circle Transform is used in this traffic light detection system with the help of Gaussian blurring and Sobel filter. So, the overall accuracy rate for the proposed algorithm is 75.59%. This system is possible to be used in urban areas or complex environments, whether it is at night or day, and it able to detect the traffic light regardless of the colour changes.

An Advanced Fuzzy Logic Based Traffic Controller

2014 ◽  
Vol 5 (1) ◽  
pp. 31-40
Author(s):  
Bilal Ahmed Khan ◽  
Nai Shyan Lai
Keyword(s):  
Image Processing ◽  
Fuzzy Logic ◽  
Traffic Management ◽  
Fuzzy Logic Controller ◽  
Current Model ◽  
Traffic Signals ◽  
Processing Technique ◽  
Urban Traffic ◽  
Image Processing Technique ◽  
Traffic Light

Traffic light plays an important role in the urban traffic management. Therefore, it is necessary to improve the traffic controller for effective traffic management and better traffic flow leading to greener environment. In this paper, an advanced and intelligent traffic light controller is proposed, utilising the fuzzy logic technology and image processing technique. A fuzzy logic control has been implemented to provide the attribute of intelligence to the system. For real-time image acquisition, the process is further linked to the fuzzy logic controller which generates a unique output for each input pattern. Here image processing and fuzzy logic tool boxes of MATLAB are used where the final output is sent to Peripheral Interface Controller (PIC) microcontroller to drive the traffic signals in the desired manner. The results obtained show an improvement of 44% in the overall outcome of traffic management as compared to the conventional traffic controller, marking great feasibility and practicality of the current model.

scholarly journals An Image Processing Based Fungus Detection System for Mangoes

2019 ◽  
Vol 9 (1) ◽  
pp. 3493-3497
Keyword(s):  
Image Processing ◽  
Detection System ◽  
Health Problems ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
High Yield ◽  
Data Set ◽  
Different Color ◽  
The Given

Fruits which grow with high yield in many states of India are rich in proteins. But due to addition of excess pesticides and chemicals intake of these fruits lead to serious health problems. It is necessary to identify the presence of chemical in the fruits before consuming it. In this project we have planned to develop an image processing technique to analyze whether the fruit is free from chemicals and fungus. In our paper, we have implemented MATLAB used as well as fungus present in the fruit. We capture the images of the fruit or we use datasets and train the database with different color-based changes that happen after adding chemicals to the fruit. The enhancement process is carried out in the captured image. Then image is segmented to hit the regions with affected spots in the fruit. K-means method is used to carry out the segmentation process. The input image is compared with the given data set for training to identify the images. In this way unhealthy fruits can be identified and the affected spots in the fruit can be detected.

An Advanced Fuzzy Logic Based Traffic Controller

2015 ◽  
pp. 1490-1499
Author(s):  
Bilal Ahmed Khan ◽  
Nai Shyan Lai
Keyword(s):  
Image Processing ◽  
Fuzzy Logic ◽  
Traffic Management ◽  
Fuzzy Logic Controller ◽  
Current Model ◽  
Traffic Signals ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Traffic Light ◽  
Logic Control

Traffic light plays an important role in the urban traffic management. Therefore, it is necessary to improve the traffic controller for effective traffic management and better traffic flow leading to greener environment. In this paper, an advanced and intelligent traffic light controller is proposed, utilising the fuzzy logic technology and image processing technique. A fuzzy logic control has been implemented to provide the attribute of intelligence to the system. For real-time image acquisition, the process is further linked to the fuzzy logic controller which generates a unique output for each input pattern. Here image processing and fuzzy logic tool boxes of MATLAB are used where the final output is sent to Peripheral Interface Controller (PIC) microcontroller to drive the traffic signals in the desired manner. The results obtained show an improvement of 44% in the overall outcome of traffic management as compared to the conventional traffic controller, marking great feasibility and practicality of the current model.

scholarly journals Application of traffic conflict technique for traffic safety evaluation at intersection based on image processing

2021 ◽  
Vol 7 (3A) ◽  
pp. 134-142
Author(s):  
Ngoc Dung Bui ◽  
Dinh Tran Ngoc Huy ◽  
Tuan Thanh Nguyen
Keyword(s):  
Image Processing ◽  
Traffic Safety ◽  
Traffic Accidents ◽  
Comprehensive Evaluation ◽  
Fuzzy Comprehensive Evaluation ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Economic Damage ◽  
Traffic Light ◽  
Safety Level

Traffic accidents occur frequently at intersections area because of conflicts among vehicles. Many researchers have been assessing traffic safety, however most of them are based on accident data that happened and caused injury as well as economic damage. Recently, conflict techniques provide general views and solutions to prevent early collisions. This paper proposes a method of conflict analysis using image processing technique and fuzzy comprehensive evaluation. Based on vehicles detection, the conflict parameters collected from two intersections in Hanoi, Vietnam were processed and evaluated by fuzzy comprehensive evaluation to give out the safety level of conflict points. The experimental results show that the proposed methods have successfully shown the distribution and location of dangerous conflict points according to the actual situation of the two intersections. Based on our results, authorities can consider reorganizing traffic light pattern at these intersections to reduce possible collisions.

Deep learning based intelligence cognitive vision drone for automatic plant diseases identification and spraying

2020 ◽  
Vol 39 (6) ◽  
pp. 8103-8114
Author(s):  
Ghazanfar Latif ◽  
Jaafar Alghazo ◽  
R. Maheswar ◽  
V. Vijayakumar ◽  
Mohsin Butt
Keyword(s):  
Image Processing ◽  
Weather Conditions ◽  
Precision Farming ◽  
Processing Technique ◽  
Plant Diseases ◽  
Image Processing Technique ◽  
National Budget ◽  
Agriculture Industry ◽  
Average Accuracy ◽  
Integral Role

The agriculture industry is of great importance in many countries and plays a considerable role in the national budget. Also, there is an increased interest in plantation and its effect on the environment. With vast areas suitable for farming, countries are always encouraging farmers through various programs to increase national farming production. However, the vast areas and large farms make it difficult for farmers and workers to continually monitor these broad areas to protect the plants from diseases and various weather conditions. A new concept dubbed Precision Farming has recently surfaced in which the latest technologies play an integral role in the farming process. In this paper, we propose a SMART Drone system equipped with high precision cameras, high computing power with proposed image processing methodologies, and connectivity for precision farming. The SMART system will automatically monitor vast farming areas with precision, identify infected plants, decide on the chemical and exact amount to spray. Besides, the system is connected to the cloud server for sending the images so that the cloud system can generate reports, including prediction on crop yield. The system is equipped with a user-friendly Human Computer Interface (HCI) for communication with the farm base. This multidrone system can process vast areas of farmland daily. The Image processing technique proposed in this paper is a modified ResNet architecture. The system is compared with deep CNN architecture and other machine learning based systems. The ResNet architecture achieves the highest average accuracy of 99.78% on a dataset consisting of 70,295 leaf images for 26 different diseases of 14 plants. The results obtained were compared with the CNN results applied in this paper and other similar techniques in previous literature. The comparisons indicate that the proposed ResNet architecture performs better compared to other similar techniques.

Design of Lighting Intelligent Control System Based on OpenCV Image Processing Technology

2021 ◽  
Vol 29 (Supp01) ◽  
pp. 119-139
Author(s):  
Suning Gong ◽  
Rakesh Kumar ◽  
D. Kumutha
Keyword(s):  
Image Processing ◽  
Control System ◽  
Traffic Flow ◽  
Public Transportation ◽  
Traffic Control ◽  
Urban Areas ◽  
Light Signal ◽  
Image Processing Technique ◽  
Traffic Density ◽  
Traffic Light

The high growth of vehicular travel in urban areas, in particular, requires a traffic control system that optimizes traffic flow efficiency. Traffic congestion can also occur by large de-lays in Red Light etc. The delay in lighting is difficult to code and does not rely on real traffic density. It follows that traffic controls are simulated and configured to better meet this rising demand. So, in order to avoid the traffic control problem, the Adaptive Intelligent Traffic Light control system (AITLCS) has been proposed based on OpenCV and Image processing technique. The system proposed is designed to ensure smooth and efficient traffic flow for daily life as well as emergency and public transportation safety. Based on the road density instead of the levels set the proposed system provides the timing for the traffic light signal so that a highly loaded side switched on over long periods compared with the other lanes. It can also be used at an intersection with traffic signs, which controls the traffic light signal at the intersection. If timers are smart to predict the exact time, the system is more efficient because it reduces the time spent on unintended green signal significantly. With the help of OpenCV software, this paper aims to have a signal management SMART solution that will be cost-effective at the end. The system consists of a camera facing a lane taking pictures of the route we want to travel and then the density of the pedestrian and vehicle is taken and compared with each image employing image processing. Such images are processed effectively to learn the density of traffic.

Sign in / Sign up

Export Citation Format

Share Document