Sub-scene Target Detection and Recognition Using Deep Learning Convolution Neural Networks

2020 ◽  
pp. 1082-1101
Author(s):  
Suresh Merugu ◽  
Kamal Jain ◽  
Anshul Mittal ◽  
Balasubramanian Raman
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Target Detection ◽  
Convolution Neural Networks ◽  
Detection And Recognition

scholarly journals Comparative Analysis on Deep Learning Approaches for Heavy-Vehicle Detection based on Data Augmentation and Transfer-Learning techniques

2021 ◽  
Vol 13 (3) ◽  
pp. 809-820
Author(s):  
V. Sowmya ◽  
R. Radha
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Real Time ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Traffic Management ◽  
Data Augmentation ◽  
Vehicle Detection ◽  
Heavy Vehicles ◽  
Detection And Recognition

Vehicle detection and recognition require demanding advanced computational intelligence and resources in a real-time traffic surveillance system for effective traffic management of all possible contingencies. One of the focus areas of deep intelligent systems is to facilitate vehicle detection and recognition techniques for robust traffic management of heavy vehicles. The following are such sophisticated mechanisms: Support Vector Machine (SVM), Convolutional Neural Networks (CNN), Regional Convolutional Neural Networks (R-CNN), You Only Look Once (YOLO) model, etcetera. Accordingly, it is pivotal to choose the precise algorithm for vehicle detection and recognition, which also addresses the real-time environment. In this study, a comparison of deep learning algorithms, such as the Faster R-CNN, YOLOv2, YOLOv3, and YOLOv4, are focused on diverse aspects of the features. Two entities for transport heavy vehicles, the buses and trucks, constitute detection and recognition elements in this proposed work. The mechanics of data augmentation and transfer-learning is implemented in the model; to build, execute, train, and test for detection and recognition to avoid over-fitting and improve speed and accuracy. Extensive empirical evaluation is conducted on two standard datasets such as COCO and PASCAL VOC 2007. Finally, comparative results and analyses are presented based on real-time.

scholarly journals DEEP LEARNING FOR CODED TARGET DETECTION

2021 ◽  
Vol XLIV-2/W1-2021 ◽  
pp. 125-130
Author(s):  
V. V. Kniaz ◽  
L. Grodzitskiy ◽  
V. A. Knyaz
Keyword(s):  
Deep Learning ◽  
Target Detection ◽  
Camera Calibration ◽  
Light Conditions ◽  
Low Light ◽  
Convolutional Network ◽  
Decoding Algorithms ◽  
Wide Range ◽  
Optical Markers ◽  
Detection And Recognition

Abstract. Coded targets are physical optical markers that can be easily identified in an image. Their detection is a critical step in the process of camera calibration. A wide range of coded targets was developed to date. The targets differ in their decoding algorithms. The main limitation of the existing methods is low robustness to new backgrounds and illumination conditions. Modern deep learning recognition-based algorithms demonstrate exciting progress in object detection performance in low-light conditions or new environments. This paper is focused on the development of a new deep convolutional network for automatic detection and recognition of the coded targets and sub-pixel estimation of their centers.

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