An Image Processing Approach to Detect Lanes, Pot Holes and Recognize Road Signs in Indian Roads

Danti, Ajit; Kulkarni, Jyoti Y.; Hiremath, P. S.

doi:10.7763/ijmo.2012.v2.204

Cited by 34 publications

(9 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Researchers in [10], in order to evaluate fish freshness conducted various subgroup studies under controlled storage conditions pertaining to sensory analysis, microbiology, volatile compounds, proteins, lipids, adenosine triphosphate and physical measurements corresponding to different stages of fish freshness .It emphasized on the rapid assessment by building mathematical models expressing storage condition effects corresponding to aforementioned areas of study used for research. [1] proposed to detect freshness of a fish using a multi color biosensor based on the etching of gold Nano rods (GNRs). Researchers used Hypoxanthine (Hx) as the index of fish freshness which in presence of xanthine oxidase (XOD) reacts with dissolved oxygen to produce H2O2 which causes GNRs to be etched quickly if Fe2+ is present This leads to regulated surface plasmon resonance (SPR) and results in distinctly color change of the system, which is easily distinguishable with the naked eye.…”

Section: Literature Surveymentioning

confidence: 99%

See 1 more Smart Citation

An Effective Technique for Determining Fish Freshness using Image Processing

2019

IJITEE

View full text Add to dashboard Cite

This paper presents an efficient image processing based algorithm to determine the freshness of a fish. The determination is made after judging the condition of the eyes and the gills of the sample. The method is applicable for most edible fishes and can be easily reconfigured by changing the effect that the two determinants have on the final outcome. The novelty of the method lies in the ability of the algorithm to easily adapt to new samples which is a result of the universal applicability of the parameters selected. The method shows highly accurate results when compared to the ground truth.

show abstract

Section: Literature Surveymentioning

confidence: 99%

“…It is a highly practical field where we teach digital systems, using camera sensors to identify visual triggers and then subsequently automate tasks. The task might be as trivial as detecting road signs, as proposed by Danti and Kilkarni [1] or it can be highly specific like watermarking images as proposed by Yadav [2].…”

Section: Introductionmentioning

confidence: 99%

An Effective Technique for Determining Fish Freshness using Image Processing

2019

IJITEE

View full text Add to dashboard Cite

show abstract

“…Danti et al [21] suggested using the k-means clustering method on the pothole images obtained using the segmentation method in the road images. The classification performance of that method was 70 %.…”

Section: Related Workmentioning

confidence: 99%

Recognition of Road Type and Quality for Advanced Driver Assistance Systems with Deep Learning

Tümen¹,

Yıldırım²,

Ergen³

2018

ElAEE

View full text Add to dashboard Cite

To develop effective advanced driving assistance systems, it is important to accurately recognize current driving environments and make critical decisions about driving processes. Preventing accidents through the interaction between the driving assistance systems and the environment and ensuring optimum driving dynamics are the main topics in this field. Vehicles need to recognize the road type and quality at a high accuracy to ensure the most suitable driving for the road type. It is also important to use both uncomplicated and cost-effective systems when performing this detection. In this study, a deep learning-based approach that can be used in vehicle driver assistance systems is proposed to automatically recognize road type and quality. Using this approach, it is possible to determine the road type and the quality of the road using only driving images as the input data. A new convolutional neural network model is designed for classification of the driving images. Driving images obtained from Google Street View are used to evaluate the recognition system for an actual driving environment. The proposed approach shows that the road types were determined with accuracy of 91.41 %, and the pothole road-smooth road distinction was successful at 91.07 %. It can be said that the proposed method is an effective structure that can be used for advanced driving support systems, V2I communications systems, and similar intelligent transportation systems.

show abstract

“…As the development of IT technology has been consistently accelerated, studies have been actively conducted to detect potholes that may threaten the safety of road users automatically using such technology [2][3][4][5][6]. Initially, acceleration sensors were mounted on vehicles and the displacement values of sensors were measured to detect potholes [3,4].…”

Section: Introductionmentioning

confidence: 99%

PotholeEye+: Deep-Learning Based Pavement Distress Detection System toward Smart Maintenance

Park¹,

Lee²,

Bang³

2021

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

We propose a mobile system, called PotholeEye + , for automatically monitoring the surface of a roadway and detecting the pavement distress in real-time through analysis of a video. PotholeEye + pre-processes the images, extracts features, and classifies the distress into a variety of types, while the road manager is driving. Every day for a year, we have tested PotholeEye + on real highway involving real settings, a camera, a mini computer, a GPS receiver, and so on. Consequently, PotholeEye + detected the pavement distress with accuracy of 92%, precision of 87% and recall 74% averagely during driving at an average speed of 110 km/h on a real highway.

show abstract

An Image Processing Approach to Detect Lanes, Pot Holes and Recognize Road Signs in Indian Roads

Cited by 34 publications

References 3 publications

An Effective Technique for Determining Fish Freshness using Image Processing

An Effective Technique for Determining Fish Freshness using Image Processing

Recognition of Road Type and Quality for Advanced Driver Assistance Systems with Deep Learning

PotholeEye+: Deep-Learning Based Pavement Distress Detection System toward Smart Maintenance

Contact Info

Product

Resources

About