Moving vehicle detection using deep neural network

Soin, Akhil; Chahande, Manisha

doi:10.1109/icetcct.2017.8280336

Cited by 13 publications

(4 citation statements)

References 12 publications

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“…In another study, Du et al [89] proposed a DL framework for car detection, which fuses LIDAR and camera. Soin and Chahande [90] proposed moving vehicles detection on-road for driverless car assistance systems based on R-CNN. They claimed that the method was achieved 100% accuracy with respect to detection accuracy on the CIFAR-10 dataset.…”

Section: Autonomous Vehiclesmentioning

confidence: 99%

Derin Öğrenme Araştırma Alanlarının Literatür Taraması

Yapıcı¹,

Tekerek²,

Topaloğlu³

2019

GMBD

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Derin öğrenme (Deep Learning-DL), birçok alanda önemli başarılar elde etmiş güçlü bir makine öğrenmesi yöntemidir. Özellikle son on yılda, bilgisayarlı görü, nesne tanıma, konuşma tanıma, doğal dil işleme gibi birçok araştırma alanında başarılı sonuçlar elde ederek, yapay zekanın derin uykudan uyanmasına yol açmıştır. Günümüzde, çeşitli alanlardaki birçok araştırmacı, DL yöntemlerini kullanarak alanlarında en iyi sonucu almaya çalışmaktadır. Bu tarama çalışmasında, DL modelleri ve DL ile çalışılabilecek önemli araştırma konuları hakkında bilgiler vererek araştırmacılara rehberlik etmeyi hedefliyoruz. Çalışmada Özerk Araçlar (Autonomous Vehicles), Doğal Dil İşleme (Natural Language Processing), El Yazısı Karakter Tanıma (Handwritten Character Recognition), İmza Doğrulama (Signature Verification), Ses ve Video Tanıma (Voice and Video Recognition), Tıbbi Görüntü İşleme (Medical İmage Processing), Büyük Veri (Big Data) gibi dünyanın en popüler ve en zorlu alanlarında yapılan DL çalışmalarını inceliyoruz. Ayrıca, araştırmacılara yardımcı olmak için, incelediğimiz bu alanlardaki DL ile çalışılabilecek, henüz çalışılmamış veya yeterince iyi sonuçlar elde edilememiş problemlere dikkat çekerek olası araştırma konularını listeliyoruz. Günümüzde, Derin öğrenme yöntemleri nesne tanıma alanında insanlardan daha iyi sonuçlara ulaşmıştır. DL üzerine yapılan çalışmalar dikkate alındığında, bu başarının otonom araçlar, tıbbi görüntü işleme, büyük veri analizi ve karakter tanıma gibi alanlarda da gerçekleşeceği öngörülmektedir. https://dx.Deep learning (DL) is an important machine learning field that has achieved considerable success in many research areas. In the last decade, the-state-of-the-art studies on many research areas such as computer vision, object recognition, speech recognition, and natural language processing were especially led to the awakening of the artificial intelligence from deep sleep. Nowadays, many researchers try to find solutions to many problems in various fields under the light of DL methods. In this study, it is presented important knowledge to guide about DL models and challenging topics that can be used in DL for researchers. This study investigated DL studies which are made in the most popular and challenging fields such as autonomous vehicles, natural language processing, handwritten character recognition, signature verification, voice and video recognition, medical image processing, and big data. Furthermore, this study points out the remaining challenges of these research areas that can be solved by DL, and discusses future topics to help the researchers. In the present day, Deep learning methods have reached better results than humans in object recognition. According to the literature studies on DL, It is foreseen that this success will be achieved in areas such as autonomous vehicles, medical image processing, big data analysis, and character recognition. https://dx.

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Section: Autonomous Vehiclesmentioning

confidence: 99%

Derin Öğrenme Araştırma Alanlarının Literatür Taraması

Yapıcı¹,

Tekerek²,

Topaloğlu³

2019

GMBD

View full text Add to dashboard Cite

show abstract

“…In addition to public transport, it is necessary to build and maintain infrastructure for pedestrians and cyclists since they help reduce the number of vehicles [17]. The second way to solve the problem is widening roads (both increasing the number of lanes as well as building new roads), building multi-level intersections and city bypasses that divert transit traffic outside the central part of cities or outside cities in general [18,19]. Though this is an effective solution because it is a very expensive one it is often viewed as unfeasible [6,20].…”

Section: Introductionmentioning

confidence: 99%

The impact of actuated control on the environment and the traffic flow

Hájnik¹,

Kalašová²,

Harantová³

et al. 2022

J Appl Eng Science

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In our paper, we have analyzed and compared fixed and actuated control at a chosen intersection, where we pointed out the importance of actuated control and its benefits. We have used traffic data from sensors in the roadway. The intersection was modelled in Aimsun, where we performed simulations. The research focused mainly on the impact of actuated control on the basic characteristics of the traffic flow, delay time and emissions. The outputs of simulations showed positive results of actuated control in all compared values. The environmental pollution topic is up-to-date and road transport has a significant impact on it. Furthermore, we want to continue with our research to investigate the impact of speed changes on emission production and the smoothness of the traffic flow under fixed and actuated control.

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“…The first task is to process one object once. We believe that the problem is similar to the problem of vehicle detection in a complex driving environment [20][21][22][23].…”

mentioning

confidence: 98%

“…They suggested using an indicator to avoid excessive background refresh. A. Soin and M. Chahande [22] found solutions to vehicle detection and recognition using deep neural networks (DNNs).…”

mentioning

confidence: 99%

Development of an Optimal Algorithm for Detecting Damaged and Diseased Potato Tubers Moving along a Conveyor Belt Using Computer Vision Systems

et al. 2021

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The article discusses the problem of detecting sick or mechanically damaged potatoes using machine learning methods. We proposed an algorithm and developed a system for the rapid detection of damaged tubers. The system can be installed on a conveyor belt in a vegetable store, and it consists of a laptop computer and an action camera, synchronized with a flashlight system. The algorithm consists of two phases. The first phase uses the Viola-Jones algorithm, applied to the filtered action camera image, so it aims to detect separate potato tubers on the conveyor belt. The second phase is the application of a method that we choose based on video capturing conditions. To isolate potatoes infected with certain types of diseases (dry rot, for example), we use the Scale Invariant Feature Transform (SIFT)—Support Vector Machine (SVM) method. In case of inconsistent or weak lighting, the histogram of oriented gradients (HOG)—Bag-of-Visual-Words (BOVW)—neural network (BPNN) method is used. Otherwise, Otsu’s threshold binarization—a convolutional neural network (CNN) method is used. The first phase’s result depends on the conveyor’s speed, the density of tubers on the conveyor, and the accuracy of the video system. With the optimal setting, the result reaches 97%. The second phase’s outcome depends on the method and varies from 80% to 97%. When evaluating the performance of the system, it was found that it allows to detect and classify up to 100 tubers in one second, which significantly exceeds the performance of most similar systems.

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Moving vehicle detection using deep neural network

Cited by 13 publications

References 12 publications

Derin Öğrenme Araştırma Alanlarının Literatür Taraması

Derin Öğrenme Araştırma Alanlarının Literatür Taraması

The impact of actuated control on the environment and the traffic flow

Development of an Optimal Algorithm for Detecting Damaged and Diseased Potato Tubers Moving along a Conveyor Belt Using Computer Vision Systems

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