An Embedded Real-Time Object Detection and Measurement of its Size

Othman, Nashwan Adnan; Salur, Mehmet Umut; Karaköse, Mehmet; Aydın, İlhan

doi:10.1109/idap.2018.8620812

Cited by 49 publications

(7 citation statements)

References 12 publications

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“…The proposed system [5] comprises two main components: object detection and object measurement. Using a Raspberry Pi camera, frames are captured for processing.…”

Section: Literature Surveymentioning

confidence: 99%

Waste Segregation using CNN

Gudgude,

Gupta,

Halade

et al. 2024

Advancements in Communication and Systems

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Waste Segregation and Disposal has become a crucial mission for sustainable socio-economic development and preserving the environment around us. Population in metropolitan and suburban areas has been increasing exponentially which has made this topic more critical since the past decade. Considering the unsurmountable pressure on current infrastructure put in place for waste segregation, upgradation and improvisation is a necessity .The integration of Machine Learning for this purpose will greatly help in addressing this purpose. Numerous Machine Learning techniques have been implemented to come up with an insightful solution . A CNN (Convolutional Neural Network) consists of n neurons which are interconnected with each other to form a network which provides us with an accurate classification model. This revolutionary technique of waste disposal is many folds faster than current waste segregation techniques which is mostly manual and time-consuming. The resultant output of these new systems will not only be useful in this step of segregation, it will also boost the process of recycling and make it more efficient and effective.

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“…The proposed system [5] comprises two main components: object detection and object measurement. Using a Raspberry Pi camera, frames are captured for processing.…”

Section: Literature Surveymentioning

confidence: 99%

Waste Segregation using CNN

Gudgude,

Gupta,

Halade

et al. 2024

Advancements in Communication and Systems

View full text Add to dashboard Cite

show abstract

“…Xie and Lu also summarize the process for object detection, applied in the detection/identification of the number of copper ores in a wire [25]. Othman et al propose an algorithm to detect the dimensions of an object in real time, using OpenCV library [26]. Hussin et al propose also an algorithm to detect an object in real time, in this case a mango, using MATLAB [27].…”

Section: Object Detection and Dimension Estimation In Imagesmentioning

confidence: 99%

Non-Destructive Fast Estimation of Tree Stem Height and Volume Using Image Processing

et al. 2021

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Measuring biometric tree characteristics to estimate the volume of wood in a forest area is a time consuming task. It is usually performed by a team of two or more people, who measure the diameter and height of several trees in sampling plots. The results are then extrapolated for the forest stand. The present paper describes a method which facilitates estimating tree biometric parameters using computational techniques. A camera takes two pictures of each sample tree, with an especially designed target placed close to the tree, to facilitate image processing and camera calibration steps. Taking advantage of the trees’ natural shape and assuming a symmetric stem, the diameter and height of the tree stems are estimated from the images and the volumes of the tree stems are calculated. Experimental trials show promising results, exhibiting errors similar to the traditional methods used currently, in the range of 10%, showing that the method is suitable for forest inventory.

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“…These partitions would help understanding the area around to improve the movement planning purposes. Semantic segmentation via deep learning (DL) is now a crucial task in computer vision, with applications including scene understanding, robotic perception, and image compression [19][20][21][22][23][24][25][26]. Semantic segmentation will precisely define semantic classifications such as buildings, transportation infrastructure, trees, and low vegetation as imaging technology advances [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

An Ultra Fast Semantic Segmentation Model for AMR’s Path Planning

Tran,

Dang

2023

Journal of Robotics and Control

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Computer vision plays a significant role in mobile robot navigation due to the abundance of information extracted from digital images. On the basis of the captured images, mobile robots determine their location and proceed to the desired destination. Obstacle avoidance still requires a complex sensor system with a high computational efficiency requirement due to the complexity of the environment. This research provides a real-time solution to the issue of extracting corridor scenes from a single image. Using an ultra-fast semantic segmentation model to reduce the number of training parameters and the cost of computation. In addition, the mean Intersection over Union (mIoU) is 89%, and the high accuracy is 95%. To demonstrate the viability of the prosed method, the simulation results are contrasted to those of contemporary techniques. Finally, the authors employ the segmented image to construct the frontal view of the mobile robot in order to determine the available free areas for mobile robot path planning tasks.

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An Embedded Real-Time Object Detection and Measurement of its Size

Cited by 49 publications

References 12 publications

Waste Segregation using CNN

Waste Segregation using CNN

Non-Destructive Fast Estimation of Tree Stem Height and Volume Using Image Processing

An Ultra Fast Semantic Segmentation Model for AMR’s Path Planning

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