Recent advances in the deep CNN neocognitron

Fukushima, Kunihiko

doi:10.1587/nolta.10.304

Cited by 18 publications

(7 citation statements)

References 14 publications

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“…Deep convolution neural networks are successfully used in recognition of visual patters. The neocognitron also belongs to the category of deep convolution neural networks [10,11,12]. The author presents recognition of partly occluded patterns, the mechanism of selective attention, increasing robustness against background noise, new learning rules as margined winner take all.…”

Section: Related Workmentioning

confidence: 99%

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Automated Access Control via License Plate Recognition using Neocognitron Neural Network

Rothkrantz

2022

2022 International Conference on Information Technologies (InfoTech)

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In 1979 Fukushima developed a hierarchical, multilayered neural network called Neocognitron and used it for the automatic recognition of handwritten Japanese symbols. We combined the Neocognitron classifier with a special image and segment processor and applied the system in 2001 for automatic recognition of license plates in laboratory experiments. In this paper we report about a special image acquisition module and a postprocessor. We tested the system in real life conditions in an application of automated access control.

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Section: Related Workmentioning

confidence: 99%

“…In this paper we discuss an application of our developed system for license plate recognition based on the Neocognitron. The original version of the system developed in 2001 has been improved in the course of the years after improvements of Fukushima [10,11]. We applied the system for admission control of a military area.…”

Section: Introductionmentioning

confidence: 99%

Automated Access Control via License Plate Recognition using Neocognitron Neural Network

Rothkrantz

2022

2022 International Conference on Information Technologies (InfoTech)

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“…This design allowed the computer to recognize visual patterns by learning about the shapes of objects [82]. CNN achieved great success not only in powering vision in robots and self-driving cars but also in identifying objects, faces, traffic signs [83] etc. It has been widely adopted in the applications for image classification, speech recognition, video classification, action recognition, and sentence classification.…”

Section: A Convolution Neural Network (Cnn)mentioning

confidence: 99%

“…CNNs, often called ConvNet, have two main components; the feature extraction part and the classification part [83], [84]. In the feature extraction part, features are detected by performing a series of convolutions and pooling operations using two hidden layers; Convolution and Pooling layers.…”

Section: A Convolution Neural Network (Cnn)mentioning

confidence: 99%

Detecting Human Driver Inattentive and Aggressive Driving Behavior Using Deep Learning: Recent Advances, Requirements and Open Challenges

2020

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Human drivers have different driving styles, experiences, and emotions due to unique driving characteristics, exhibiting their own driving behaviors and habits. Various research efforts have approached the problem of detecting abnormal human driver behavior with the aid of capturing and analyzing the face of driver and vehicle dynamics via image and video processing but the traditional methods are not capable of capturing complex temporal features of driving behaviors. However, with the advent of deep learning algorithms, a significant amount of research has also been conducted to predict and analyze driver's behavior or action related information using neural network algorithms. In this paper, we contribute to first classify and discuss Human Driver Inattentive Driving Behavior (HIDB) into two major categories, Driver Distraction (DD), Driver Fatigue (DF), or Drowsiness (DFD). Then we discuss the causes and effects of another human risky driving behavior called Aggressive Driving behavior (ADB). Aggressive driving Behavior (ADB) is a broad group of dangerous and aggressive driving styles that lead to severe accidents. Human abnormal driving behaviors DD, DFD, and ADB are affected by various factors including driver experience/inexperience of driving, age, and gender or illness. The study of the effects of these factors that may lead to deterioration in the driving skills and performance of a human driver is out of the scope of this paper. After describing the background of deep learning and its algorithms, we present an in-depth investigation of most recent deep learning-based systems, algorithms, and techniques for the detection of Distraction, Fatigue/Drowsiness, and Aggressiveness of a human driver. We attempt to achieve a comprehensive understanding of HIADB detection by presenting a detailed comparative analysis of all the recent techniques. Moreover, we highlight the fundamental requirements. Finally, we present and discuss some significant and essential open research challenges as future directions.

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“…DL has emerged as a highly effective machine learning methodology that has revolution- [ 4 ]. In this research, we build upon one of the advanced object detection models, EfficientDet, originally developed by the Google Brain Team.…”

Section: Introductionmentioning

confidence: 99%