scholarly journals BEAM or MFA I inspired Nv Neurons using op amps for line and line based polygon detection.

2020 ◽  
Author(s):  
Anil Kumar Bheemaiah
Keyword(s):  
International Workshop ◽  
Region Of Interest ◽  
Simple Polygon ◽  
Term Rewriting ◽  
Line Detection ◽  
Large Signal ◽  
Traffic Sign ◽  
Op Amp ◽  
Stop Sign ◽  
Functional Art

Abstract: Tensor network topologies for function, first class or MFA I as BEAM circuits are described within the framework of complexity theory using Lie Computability definitions. An example of the design of opamp based Nv Neurons for the perception of shape from line detection Nv neurons is described, with circuits that detect number of lines and concavity and closure of lines in a finite region of interest. The possible role of BEAM robotics in 3R’s is described in nature inspired intent transcription, in multi -functionality and functionality driven evolution and transcription. Keywords: BEAM, MFA I, MFA II, Lie Computability, op amp circuits, Nv Neurons, Two Port Systems, large signal analysis, feedback principles, solitons, neuro-modulation. What: Nv Neurons are built from opamp based circuits, for line detection using an array or grid of inexpensive photo detectors, using an opamp based positive feedback loop and negative feedback loops for synergy principles. Story: The author first worked on this problem in his undergraduate senior year, when his advisor advised a bottom-up approach to BEAM based machine vision, biomimetics in synthetic neurons from discrete components and opamps. The problem was to differentiate a simple polygonal shape from the background. How: A simple polygon is found in traffic sign posts , creating the need for a hard wired circuit to recognize an octahedral stop sign and several triangular signs. We use line decomposition with a circuitry to compose the lines into polyhedral shapes.(Bheemaiah, n.d.) Why: BEAM is functional art, and forms the predecessor to MFA II or completely multi functional architecture of a broad umbrella of value addition in multi functionality, functoid and HOF based algebraic frameworks for MaC based definitions of architecture and design in code.(Autores and International Workshop on Higher-Order Algebra, Logic and Term Rewriting 1994; Kirchner and Wechler 1990; Dowek et al. 1996)

scholarly journals BEAM or MFA I inspired Nv Neurons using op amps for line and line based polygon detection.

2020 ◽  
Author(s):  
Anil Kumar Bheemaiah
Keyword(s):  
International Workshop ◽  
Region Of Interest ◽  
Simple Polygon ◽  
Term Rewriting ◽  
Line Detection ◽  
Large Signal ◽  
Traffic Sign ◽  
Op Amp ◽  
Stop Sign ◽  
Functional Art

Abstract: Tensor network topologies for function, first class or MFA I as BEAM circuits are described within the framework of complexity theory using Lie Computability definitions. An example of the design of opamp based Nv Neurons for the perception of shape from line detection Nv neurons is described, with circuits that detect number of lines and concavity and closure of lines in a finite region of interest. The possible role of BEAM robotics in 3R’s is described in nature inspired intent transcription, in multi -functionality and functionality driven evolution and transcription. Keywords: BEAM, MFA I, MFA II, Lie Computability, op amp circuits, Nv Neurons, Two Port Systems, large signal analysis, feedback principles, solitons, neuro-modulation. What: Nv Neurons are built from opamp based circuits, for line detection using an array or grid of inexpensive photo detectors, using an opamp based positive feedback loop and negative feedback loops for synergy principles. Story: The author first worked on this problem in his undergraduate senior year, when his advisor advised a bottom-up approach to BEAM based machine vision, biomimetics in synthetic neurons from discrete components and opamps. The problem was to differentiate a simple polygonal shape from the background. How: A simple polygon is found in traffic sign posts , creating the need for a hard wired circuit to recognize an octahedral stop sign and several triangular signs. We use line decomposition with a circuitry to compose the lines into polyhedral shapes.(Bheemaiah, n.d.) Why: BEAM is functional art, and forms the predecessor to MFA II or completely multi functional architecture of a broad umbrella of value addition in multi functionality, functoid and HOF based algebraic frameworks for MaC based definitions of architecture and design in code.(Autores and International Workshop on Higher-Order Algebra, Logic and Term Rewriting 1994; Kirchner and Wechler 1990; Dowek et al. 1996)

The Traffic Sign Detection Algorithm Based on Region of Interest Extraction and Double Filter

2019 ◽  
Vol 13 ◽  
Author(s):  
Dongxian Yu ◽  
Jiatao Kang ◽  
Zaihui Cao ◽  
Neha Jain
Keyword(s):  
Region Of Interest ◽  
Detection Algorithm ◽  
Light Filter ◽  
Support Vector ◽  
Detection Accuracy ◽  
Traffic Light ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Detection ◽  
Traffic Sign Detection

In order to solve the current traffic sign detection technology due to the interference of various complex factors, it is difficult to effectively carry out the correct detection of traffic signs, and the robustness is weak, a traffic sign detection algorithm based on the region of interest extraction and double filter is designed.First, in order to reduce environmental interference, the input image is preprocessed to enhance the main color of each logo.Secondly, in order to improve the extraction ability Of Regions Of Interest, a Region Of Interest (ROI) detector based on Maximally Stable Extremal Regions (MSER) and Wave Equation (WE) was defined, and candidate Regions were selected through the ROI detector.Then, an effective HOG (Histogram of Oriented Gradient) descriptor is introduced as the detection feature of traffic signs, and SVM (Support Vector Machine) is used to classify them into traffic signs or background.Finally, the context-aware filter and the traffic light filter are used to further identify the false traffic signs and improve the detection accuracy.In the GTSDB database, three kinds of traffic signs, which are indicative, prohibited and dangerous, are tested, and the results show that the proposed algorithm has higher detection accuracy and robustness compared with the current traffic sign recognition technology.

Utilization of ADAS for Improving Idle Stop-and-Go Control

2018 ◽  
Author(s):  
Kwangwoo Jeong ◽  
Hoon Lee ◽  
Jaihyun Lee ◽  
Sanghoon Yoo ◽  
Byungho Lee ◽  
...  
Keyword(s):  
Control Algorithm ◽  
Traffic Information ◽  
Test Results ◽  
Driver Assistance ◽  
Control Logic ◽  
Traffic Sign ◽  
Sign Recognition ◽  
Stop Sign ◽  
Stop And Go ◽  
Vehicle Test

Idle Stop and Go (ISG), also known as Automatic Engine Stop/Start, has been widely implemented in production vehicles as one of the “Eco” functions that save fuel, and the application has been promoted to meet stringent fuel economy regulations throughout the world. However, the vibration and the hesitation caused by engine stop and restart often discourage the usage. Because a conventional ISG system usually restarts the engine when it sees the brake pedal release, the driver may perceive a delay in immediate vehicle launch. Furthermore, there are some driving conditions where engine on/off is undesirable or unnecessary. A quick stop-and-go situation such as making a complete stop at a stop sign is one of the conditions where ISG would be inappropriate, and in those cases, ISG may irritate the driver or even end up increasing fuel consumption with too frequent engine stop/start. In order to mitigate aforementioned issues, a utilization of Advanced Driver Assistance System (ADAS) is proposed. With the surrounding traffic information obtained from the ADAS module, ISG control algorithm is able to determine when to turn on or off the engine prior to driver’s input. The applications demonstrated in this paper include the following usage examples: The ISG control logic monitors the movement of the vehicle in front and restarts the engine out of ISG mode before brake release, which eliminates the delay in the following vehicle launch. By employing traffic sign recognition and vehicle location info, the control logic is also able to inhibit engine off when the vehicle stops at stop signs which will avoid unwanted ISG activation. In this paper, the advanced ISG control logic is introduced, and the real-world vehicle test results are provided with the description of prototype vehicle configuration.

scholarly journals Search Like an Eagle: A Cascaded Model for Insulator Missing Faults Detection in Aerial Images

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 713 ◽  
Author(s):  
Jiaming Han ◽  
Zhong Yang ◽  
Hao Xu ◽  
Guoxiong Hu ◽  
Chi Zhang ◽  
...  
Keyword(s):  
High Voltage ◽  
Transmission Lines ◽  
Energy Supply ◽  
Region Of Interest ◽  
Aerial Images ◽  
Line Detection ◽  
Complex Background ◽  
Average Running Time ◽  
Cascaded Model ◽  
On Line

Insulator missing fault is a serious accident of high-voltage transmission lines, which can cause abnormal energy supply. Recently, a lot of vision-based methods are proposed for detecting an insulator missing fault in aerial images. However, these methods usually lack efficiency and robustness due to the effect of the complex background interferences in the aerial images. More importantly, most of these methods cannot address the insulator multi-fault detection. This paper proposes an unprecedented cascaded model to detect insulator multi-fault in the aerial images to solve the existing challenges. Firstly, a total of 764 images are adopted to create a novel insulator missing faults dataset ‘IMF-detection’. Secondly, a new network is proposed to locate the insulator string from the complex background. Then, the located region that contains the insulator string is set to be an RoI (region of interest) region. Finally, the YOLO-v3 tiny network is trained and then used to detect the insulator missing faults in the RoI region. Experimental results and analysis validate that the proposed method is more efficient and robust than some previous works. Most importantly, the average running time of the proposed method is about 30ms, which demonstrates that it has the potential to be adopted for the on-line detection of insulator missing faults.

scholarly journals Improved Faster R-CNN Traffic Sign Detection Based on a Second Region of Interest and Highly Possible Regions Proposal Network

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2288 ◽  
Author(s):  
Faming Shao ◽  
Xinqing Wang ◽  
Fanjie Meng ◽  
Jingwei Zhu ◽  
Dong Wang ◽  
...  
Keyword(s):  
A Priori ◽  
Region Of Interest ◽  
Traffic Sign ◽  
Detection Systems ◽  
Proposal A ◽  
Sign Detection ◽  
Priori Information ◽  
Small Targets ◽  
Traffic Sign Detection ◽  
Classification Capability

Traffic sign detection systems provide important road control information for unmanned driving systems or auxiliary driving. In this paper, the Faster region with a convolutional neural network (R-CNN) for traffic sign detection in real traffic situations has been systematically improved. First, a first step region proposal algorithm based on simplified Gabor wavelets (SGWs) and maximally stable extremal regions (MSERs) is proposed. In this way, the region proposal a priori information is obtained and will be used for improving the Faster R-CNN. This part of our method is named as the highly possible regions proposal network (HP-RPN). Second, in order to solve the problem that the Faster R-CNN cannot effectively detect small targets, a method that combines the features of the third, fourth, and fifth layers of VGG16 to enrich the features of small targets is proposed. Third, the secondary region of interest method to enhance the feature of detection objects and improve the classification capability of the Faster R-CNN is proposed. Finally, a method of merging the German traffic sign detection benchmark (GTSDB) and Chinese traffic sign dataset (CTSD) databases into one larger database to increase the number of database samples is proposed. Experimental results show that our method improves the detection performance, especially for small targets.

Traffic Sign Recognition in Autonomous Vehicles Using Edge Detection

2017 ◽  
Author(s):  
Harish Vishwanathan ◽  
Diane L. Peters ◽  
James Z. Zhang
Keyword(s):  
Edge Detection ◽  
Autonomous Vehicles ◽  
Human Error ◽  
Linear Prediction ◽  
New Technology ◽  
Night Vision ◽  
Detection Methods ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Stop Sign

Autonomous vehicles have the potential to improve safety by eliminating human error in driving, as well as providing mobility to those who cannot safely drive. Such vehicles do require new technology to monitor their environment and ensure that they are operating safely. One such technology that will be necessary is the ability of the vehicle to recognize traffic situations and traffic signs. This can be accomplished by an appropriate implementation of edge detection methods. In this paper, we compare three different edge detection methods: Canny method, Sobel method and Zhang method. This comparison was conducted on both still pictures and on a video. When analyzing the video, which was taken on a clear day with an undamaged and clearly visible stop sign, all three methods performed equally well; the time at which the stop sign was identified, based on the edge map, was the same. The purpose of this comparison is to evaluate the performance of each of the three methods, in the context of the problem of identifying traffic signs. The methods are compared on still images of a stop sign under various conditions, in addition to the single video comparison. Based on the still image comparison, we conclude that Zhang’s method (linear prediction) generates the best edge map, particularly when the images include snow, ice, rain or other factors and even at night vision.

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