A Traffic Light Detection Method

Author(s):  
Chunhe Yu ◽  
Ying Bai
2017 ◽  
Vol 5 (3) ◽  
pp. 20
Author(s):  
JEBISHA J ◽  
MONISHA V ◽  
JEMI B. FEMILA ◽  
◽  
◽  
...  

Insects ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 707
Author(s):  
Qi Yao ◽  
Huining Zhang ◽  
Long Jiao ◽  
Xiaoming Cai ◽  
Manqun Wang ◽  
...  

Tea leafhopper (Empoasca onukii Matsuda) is amongst the key pests in tea plantations around the East Asian region. Stereomicroscopy is a conventional method used for detecting tea leafhopper eggs by dissecting the tender tissues. However, there is a need for a faster and more efficient method to directly observe and investigate intact eggs within tea shoots. The absence of a proven method limits research efforts for determining the oviposition behavior of E. onukii. Herein, we applied the blue light detection method (BLDM), a technique recently developed for other species, in order to detect E. onukii eggs directly and non-destructively within the tender shoot. In addition, we compared BLDM against the traditional stereomicroscope detection method (SMDM) for four tea cultivars. Notably, our results revealed that BLDM was precise and effective in measuring the egg laying quantity of E. onukii on intact tea shoots. Neither tea cultivars nor egg density in the tender shoot significantly affected the accuracy of BLDM. Furthermore, biological characteristics that have rarely been reported previously for E. onukii were investigated using the BLDM, including zygote duration, ovipositional rhythm, egg distribution within the tender shoot, and in different leaf positions, numbers of eggs laid by a single female daily, and laid by the entire generation. Therefore, these findings provide insights into the basic and theoretical evidence for the strategy and mechanism associated with the oviposition behavior of E. onukii.


Information ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 278 ◽  
Author(s):  
Thiago Almeida ◽  
Hendrik Macedo ◽  
Leonardo Matos ◽  
Nathanael Vasconcelos

Traffic light detection and recognition (TLR) research has grown every year. In addition, Machine Learning (ML) has been largely used not only in traffic light research but in every field where it is useful and possible to generalize data and automatize human behavior. ML algorithms require a large amount of data to work properly and, thus, a lot of computational power is required to analyze the data. We argue that expert knowledge should be used to decrease the burden of collecting a huge amount of data for ML tasks. In this paper, we show how such kind of knowledge was used to reduce the amount of data and improve the accuracy rate for traffic light detection and recognition. Results show an improvement in the accuracy rate around 15%. The paper also proposes a TLR device prototype using both camera and processing unit of a smartphone which can be used as a driver assistance. To validate such layout prototype, a dataset was built and used to test an ML model based on adaptive background suppression filter (AdaBSF) and Support Vector Machines (SVMs). Results show 100% precision rate and recall of 65%.


2021 ◽  
Vol 1820 (1) ◽  
pp. 012079
Author(s):  
Junyu Zhang ◽  
Xiao Jing ◽  
Zhiwen Li ◽  
Qingchun Huang

Author(s):  
S. Hosseinyalmdary ◽  
A. Yilmaz

Traffic lights detection and their state recognition is a crucial task that autonomous vehicles must reliably fulfill. Despite scientific endeavors, it still is an open problem due to the variations of traffic lights and their perception in image form. Unlike previous studies, this paper investigates the use of inaccurate and publicly available GIS databases such as OpenStreetMap. In addition, we are the first to exploit conic section geometry to improve the shape cue of the traffic lights in images. Conic section also enables us to estimate the pose of the traffic lights with respect to the camera. Our approach can detect multiple traffic lights in the scene, it also is able to detect the traffic lights in the absence of prior knowledge, and detect the traffics lights as far as 70 meters. The proposed approach has been evaluated for different scenarios and the results show that the use of stereo cameras significantly improves the accuracy of the traffic lights detection and pose estimation.


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