Deep learning for object detection and scene perception in self-driving cars: Survey, challenges, and open issues

Object Detection is one of the most important concepts of Computer Vision which is used in various areas like Medical Field, Security, Self Driving cars, Automated vehicle systems etc.We choose the application of Automatic License plate Recognition. Automatic License Plate Recognition is an emerging technology which is helpful in many fields and at the same time is challenging. It’s challenging because we need to get the accurate recognition of the characters in a number plate. In practical applications where sometimes the images are captured in the worst weather condition, bad lighting, wind. And to the addition, license plates are often dirty or blackened due to the smoke, half broken , or having scratches on certain characters and detection of too many license plates in a single frame. All these will act as the obstacles in developing an effective ALPR system. So basically, this is a system where recognition of characters from images using Computer Vision techniques are performed. This system is implemented in many fields like parking lots, private and public entrances, toll gates, theft control, checking the authentication of a vehicle. Procedure followed in this paper are, first capturing images from camera then loading that into system, preprocessing done using OpenCV library. Then we use Attention OCR a deep learning model to recognize the characters from an image. And later display that in the GUI and store them in the databases for different operations later.

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Saliency detection in deep learning era: trends of development

Information and Control Systems ◽

10.31799/1684-8853-2019-3-10-36 ◽

2019 ◽

pp. 10-36 ◽

Cited By ~ 2

Author(s):

M. N. Favorskaya ◽

L. C. Jain

Keyword(s):

Deep Learning ◽

Object Detection ◽

Event Detection ◽

Visual Analysis ◽

Saliency Detection ◽

Salient Object Detection ◽

Public Image ◽

Detection Methods ◽

Salient Object ◽

Salient Event

Introduction:Saliency detection is a fundamental task of computer vision. Its ultimate aim is to localize the objects of interest that grab human visual attention with respect to the rest of the image. A great variety of saliency models based on different approaches was developed since 1990s. In recent years, the saliency detection has become one of actively studied topic in the theory of Convolutional Neural Network (CNN). Many original decisions using CNNs were proposed for salient object detection and, even, event detection.Purpose:A detailed survey of saliency detection methods in deep learning era allows to understand the current possibilities of CNN approach for visual analysis conducted by the human eyes’ tracking and digital image processing.Results:A survey reflects the recent advances in saliency detection using CNNs. Different models available in literature, such as static and dynamic 2D CNNs for salient object detection and 3D CNNs for salient event detection are discussed in the chronological order. It is worth noting that automatic salient event detection in durable videos became possible using the recently appeared 3D CNN combining with 2D CNN for salient audio detection. Also in this article, we have presented a short description of public image and video datasets with annotated salient objects or events, as well as the often used metrics for the results’ evaluation.Practical relevance:This survey is considered as a contribution in the study of rapidly developed deep learning methods with respect to the saliency detection in the images and videos.

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COMPARATIVE ANALYSIS OF DEEP LEARNING METHODS FOR OBJECT DETECTION

Advances in Mathematics: Scientific Journal ◽

10.37418/amsj.9.6.54 ◽

2020 ◽

Vol 9 (6) ◽

pp. 3759-3775

Author(s):

K. Gill ◽

V. Mangat

Keyword(s):

Deep Learning ◽

Comparative Analysis ◽

Object Detection ◽

Learning Methods

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Design of Desktop Audiovisual Entertainment System with Deep Learning and Haptic Sensations

Symmetry ◽

10.3390/sym12101718 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1718

Author(s):

Chien-Hsing Chou ◽

Yu-Sheng Su ◽

Che-Ju Hsu ◽

Kong-Chang Lee ◽

Ping-Hsuan Han

Keyword(s):

Deep Learning ◽

Object Detection ◽

User Experience ◽

Recognition System ◽

Scene Recognition ◽

Single Shot ◽

Auditory Signals ◽

Hot Weather ◽

Viewing Experience ◽

At Home

In this study, we designed a four-dimensional (4D) audiovisual entertainment system called Sense. This system comprises a scene recognition system and hardware modules that provide haptic sensations for users when they watch movies and animations at home. In the scene recognition system, we used Google Cloud Vision to detect common scene elements in a video, such as fire, explosions, wind, and rain, and further determine whether the scene depicts hot weather, rain, or snow. Additionally, for animated videos, we applied deep learning with a single shot multibox detector to detect whether the animated video contained scenes of fire-related objects. The hardware module was designed to provide six types of haptic sensations set as line-symmetry to provide a better user experience. After the system considers the results of object detection via the scene recognition system, the system generates corresponding haptic sensations. The system integrates deep learning, auditory signals, and haptic sensations to provide an enhanced viewing experience.

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U-Infuse: Democratization of Customizable Deep Learning for Object Detection

Sensors ◽

10.3390/s21082611 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2611

Author(s):

Andrew Shepley ◽

Greg Falzon ◽

Christopher Lawson ◽

Paul Meek ◽

Paul Kwan

Keyword(s):

Deep Learning ◽

Intellectual Property ◽

Object Detection ◽

Image Data ◽

Learning Technologies ◽

Training Data ◽

Learning Models ◽

Ecological Data ◽

Single Class ◽

Large Numbers

Image data is one of the primary sources of ecological data used in biodiversity conservation and management worldwide. However, classifying and interpreting large numbers of images is time and resource expensive, particularly in the context of camera trapping. Deep learning models have been used to achieve this task but are often not suited to specific applications due to their inability to generalise to new environments and inconsistent performance. Models need to be developed for specific species cohorts and environments, but the technical skills required to achieve this are a key barrier to the accessibility of this technology to ecologists. Thus, there is a strong need to democratize access to deep learning technologies by providing an easy-to-use software application allowing non-technical users to train custom object detectors. U-Infuse addresses this issue by providing ecologists with the ability to train customised models using publicly available images and/or their own images without specific technical expertise. Auto-annotation and annotation editing functionalities minimize the constraints of manually annotating and pre-processing large numbers of images. U-Infuse is a free and open-source software solution that supports both multiclass and single class training and object detection, allowing ecologists to access deep learning technologies usually only available to computer scientists, on their own device, customised for their application, without sharing intellectual property or sensitive data. It provides ecological practitioners with the ability to (i) easily achieve object detection within a user-friendly GUI, generating a species distribution report, and other useful statistics, (ii) custom train deep learning models using publicly available and custom training data, (iii) achieve supervised auto-annotation of images for further training, with the benefit of editing annotations to ensure quality datasets. Broad adoption of U-Infuse by ecological practitioners will improve ecological image analysis and processing by allowing significantly more image data to be processed with minimal expenditure of time and resources, particularly for camera trap images. Ease of training and use of transfer learning means domain-specific models can be trained rapidly, and frequently updated without the need for computer science expertise, or data sharing, protecting intellectual property and privacy.

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