Object Detection for Autonomous Vehicles Using Deep Learning Algorithm

2021 ◽  
pp. 327-339
Author(s):  
E. J. Sai Pavan ◽  
P. Ramya ◽  
B. Valarmathi ◽  
T. Chellatamilan ◽  
K. Santhi
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Autonomous Vehicles ◽  
Learning Algorithm ◽  
Deep Learning Algorithm

scholarly journals Deep Learning Method on Target Echo Signal Recognition for Obscurant Penetrating Lidar Detection in Degraded Visual Environments

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3424
Author(s):  
Xujia Liang ◽  
Zhonghua Huang ◽  
Liping Lu ◽  
Zhigang Tao ◽  
Bing Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicles ◽  
Mobile Robotics ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Detection Methods ◽  
Signal Recognition ◽  
Echo Signal ◽  
Deep Learning Algorithm ◽  
Recognition Criterion

With the rapid development of autonomous vehicles and mobile robotics, the desire to advance robust light detection and ranging (Lidar) detection methods for real world applications is increasing. However, this task still suffers in degraded visual environments (DVE), including smoke, dust, fog, and rain, as the aerosols lead to false alarm and dysfunction. Therefore, a novel Lidar target echo signal recognition method, based on a multi-distance measurement and deep learning algorithm is presented in this paper; neither the backscatter suppression nor the denoise functions are required. The 2-D spectrogram images are constructed by using the frequency-distance relation derived from the 1-D echo signals of the Lidar sensor individual cell in the course of approaching target. The characteristics of the target echo signal and noise in the spectrogram images are analyzed and determined; thus, the target recognition criterion is established accordingly. A customized deep learning algorithm is subsequently developed to perform the recognition. The simulation and experimental results demonstrate that the proposed method can significantly improve the Lidar detection performance in DVE.

scholarly journals Effective Deep Learning Based Architecture for Pedestrian Detection from Digital Images

2020 ◽  
Vol 9 (3) ◽  
pp. 1498-1508
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Object Detection ◽  
Learning Algorithm ◽  
Pedestrian Detection ◽  
Single Shot ◽  
Deep Learning Algorithm ◽  
Learning Techniques ◽  
Rgb Images ◽  
Deep Convolution Network

This paper is to present an efficient and fast deep learning algorithm based on neural networks for object detection and pedestrian detection. The technique, called MobileNet Single Shot Detector, is an extension to Convolution Neural Networks. This technique is based on depth-wise distinguishable convolutions in order to build a lightweighted deep convolution network. A single filter is applied to each input and outputs are combined by using pointwise convolution. Single Shot Multibox Detector is a feed forward convolution network that is combined with MobileNets to give efficient and accurate results. MobileNets combined with SSD and Multibox Technique makes it much faster than SSD alone can work. The accuracy for this technique is calculated over colored (RGB images) and also on infrared images and its results are compared with the results of shallow machine learning based feature extraction plus classification technique viz. HOG plus SVM technique. The comparison of performance between proposed deep learning and shallow learning techniques has been conducted over benchmark dataset and validation testing over own dataset in order measure efficiency of both algorithms and find an effective algorithm that can work with speed and accurately to be applied for object detection in real world pedestrian detection application.

scholarly journals Vision-Based Deep Learning Algorithm for Detecting Potholes

2021 ◽  
Author(s):  
Kanushka Gajjar ◽  
Theo van Niekerk ◽  
Thomas Wilm ◽  
Paolo Mercorelli
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Learning Algorithm ◽  
Detection System ◽  
Detection Methods ◽  
Deep Learning Algorithm ◽  
Learning Techniques ◽  
Pothole Detection ◽  
Adequate Data

Potholes on roads pose a major threat to motorists and autonomous vehicles. Driving over a pothole has the potential to cause serious damage to a vehicle, which in turn may result in fatal accidents. Currently, many pothole detection methods exist. However, these methods do not utilize deep learning techniques to detect a pothole in real-time, determine the location thereof and display its location on a map. The success of determining an effective pothole detection method, which includes the aforementioned deep learning techniques, is dependent on acquiring a large amount of data, including images of potholes. Once adequate data had been gathered, the images were processed and annotated. The next step was to determine which deep learning algorithms could be utilized. Three different models, including Faster R-CNN, SSD and YOLOv3 were trained on the custom dataset containing images of potholes to determine which network produces the best results for real-time detection. It was revealed that YOLOv3 produced the most accurate results and performed the best in real-time, with an average detection time of only 0.836s per image. The final results revealed that a real-time pothole detection system, integrated with a cloud and maps service, can be created to allow drivers to avoid potholes.

scholarly journals A Moving Object Detection Method Using Deep Learning-Based Wireless Sensor Networks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Linghua Zhao ◽  
Zhihua Huang
Keyword(s):  
Wireless Sensor Networks ◽  
Deep Learning ◽  
Sensor Networks ◽  
Object Detection ◽  
Moving Objects ◽  
Detection Method ◽  
Learning Algorithm ◽  
Wireless Sensor ◽  
Moving Target ◽  
Deep Learning Algorithm

Aiming at the problem of real-time detection and location of moving objects, the deep learning algorithm is used to detect moving objects in complex situations. In this paper, based on the deep learning algorithm of wireless sensor networks, a novel target motion detection method is proposed. This method uses the deep learning model to extract visual potential representation features through offline similarity function ranking learning and online model incremental update and uses the hierarchical clustering algorithm to achieve target detection and positioning; the low-precision histogram and high-precision histogram cascade the method which determines the correct position of the target and achieves the purpose of detecting the moving target. In order to verify the advantages and disadvantages of the deep learning algorithm compared with traditional moving object detection methods, a large number of comparative experiments are carried out, and the experimental results were analyzed qualitatively and quantitatively from a statistical perspective. The results show that, compared with the traditional methods, the deep learning algorithm based on the wireless sensor network proposed in this paper is more efficient. The detection and positioning method do not produce the error accumulation phenomenon and has significant advantages and robustness. The moving target can be accurately detected with a small computational cost.

scholarly journals A Framework for Object Detection using Deep-Reinforcement Machine Learning

2020 ◽  
Vol 9 (4) ◽  
pp. 1508-1514
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Reinforcement Learning ◽  
Object Detection ◽  
Intelligent Systems ◽  
Learning Algorithm ◽  
Efficient Tool ◽  
Narrow Scope ◽  
Deep Learning Algorithm ◽  
Learning Techniques

Machine learning area enable the utilization of Deep learning algorithm and neural networks (DNNs) with Reinforcement Learning. Reinforcement learning and DL both is region of AI, it’s an efficient tool towards structuring artificially intelligent systems and solving sequential deciding problems. Reinforcement learning (RL) deals with the history of moves; Reinforcement learning problems are often resolve by an agent often denoted as (A) it has privilege to make decisions during a situation to optimize a given problem by collective rewards. Ability to structure sizable amount of attributes make deep learning an efficient tool for unstructured data. Comparing multiple deep learning algorithms may be a major issue thanks to the character of the training process and therefore the narrow scope of datasets tested in algorithmic prisons. Our research proposed a framework which exposed that reinforcement learning techniques in combination with Deep learning techniques learn functional representations for sorting problems with high dimensional unprocessed data. The faster RCNN model typically founds objects in faster way saving resources like computation, processing, and storage. But still object detection technique typically require high computation power and large memory and processor building it hard to run on resource constrained devices (RCD) for detecting an object during real time without an efficient and high computing machine.

scholarly journals Object Detection Method by using Yolov3 with Machine Learning

2020 ◽  
Vol 8 (6) ◽  
pp. 5247-5250
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Object Detection ◽  
Moving Objects ◽  
Learning Algorithm ◽  
Detection Algorithm ◽  
Video Object ◽  
Long Distance ◽  
Deep Learning Algorithm ◽  
Tracking Movement

Detecting the object is a vision technique of a computer for detectng or locating long distance or short distance objects and images. Object detection algorithm mainly works on the machine learning and the artificial intelligence algorithms. Present trending algorithm in detecting the object is deep learning algorithm, By using the deep learning algorithm we can get the accurate results of the object which is detected. it is mainly or widely used in the system vision tasks like video object co-segmentations, tracking movement of the ball in the ground, image annotating etc. Each and every object has its own features ,for example if you select the ball , Actually all the ball are in the round shape but in every game different type of balls are used ,object detection camera will detect the ball it will check the ball specifications with its data if any data was matched with its data base the system will display all the specifications of ball. By using the deep learning algorithm we introduced one new technique to detect detect object accurately the algorithm is named as the YOLO V3 we can detect the very small objects and the fastly moving objects easily. This yolo v3 will convert the image into N number of layers and it will work on the each and every minute spot on the image.

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