Vision System for Quality Assessment of Robotic Cleaning of Fish Processing Plants Using CNN

Bjørlykhaug, Emil; Egeland, Olav

doi:10.1109/access.2019.2919656

Cited by 17 publications

(10 citation statements)

References 28 publications

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“…These parameters are regarded as the extrinsic parameters of the camera. The projection of the points in the camera coordinate onto the image plane based on the pinhole camera model is given by Equation (3). The task of computing the relative 3D position and orientation between the camera and the robot hand in an eyeon-hand configuration, where the camera is rigidly attached to the robot hand, is known as hand-eye calibration.…”

Section: Hand-eye Calibrationmentioning

confidence: 99%

A Comparative Review of Hand-Eye Calibration Techniques for Vision Guided Robots

et al. 2021

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Section: Hand-eye Calibrationmentioning

confidence: 99%

A Comparative Review of Hand-Eye Calibration Techniques for Vision Guided Robots

et al. 2021

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“…A research was done on various methods and platforms for structural inspections. In the work of Bjørlykhaug and Egeland [ 17 ], a vision system was developed for automatic quality evaluation of robotic cleaning of fish processing lines. The system was based on 10 different CNN models with augmented data for processing the images.…”

Section: Related Workmentioning

confidence: 99%

Automatic System for Visual Detection of Dirt Buildup on Conveyor Belts Using Convolutional Neural Networks

Santos

Rocha

Reis

et al. 2020

Sensors

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Conveyor belts are the most widespread means of transportation for large quantities of materials in the mining sector. Therefore, autonomous methods that can help human beings to perform the inspection of the belt conveyor system is a major concern for companies. In this context, we present in this work a novel and automatic visual detector that recognizes dirt buildup on the structures of conveyor belts, which is one of the tasks of the maintenance inspectors. This visual detector can be embedded as sensors in autonomous robots for the inspection activity. The proposed system involves training a convolutional neural network from RGB images. The use of the transfer learning technique, i.e., retraining consolidated networks for image classification with our collected images has shown very effective. Two different approaches for transfer learning have been analyzed. The best one presented an average accuracy of 0.8975 with an F-1 Score of 0.8773 for the dirt recognition. A field validation experiment served to evaluate the performance of the proposed system in a real time classification task.

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“…Similarly, a cleaning assessment report generation based on surface swabbing followed by a laboratory analysis as detailed in [ 16 ]. Even though the cleaning quality assessment is regarded as an essential practice in every domain, effort towards establishing a quality assessment is reported in a few areas and is limited to hospitals, pharmaceuticals, and food processing industries [ 17 , 18 ]. Besides, the current methods for micro-biological analysis are laborious and not scalable for cleaning auditing for built infrastructure and domestic cleaning.…”

Section: Introductionmentioning

confidence: 99%

A Novel Path Planning Strategy for a Cleaning Audit Robot Using Geometrical Features and Swarm Algorithms

Pathmakumar

Muthugala

Samarakoon

et al. 2022

Sensors

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Robot-aided cleaning auditing is pioneering research that uses autonomous robots to assess a region’s cleanliness level by analyzing the dirt samples collected from various locations. Since the dirt sample gathering process is more challenging, adapting a coverage planning strategy from a similar domain for cleaning is non-viable. Alternatively, a path planning approach to gathering dirt samples selectively at locations with a high likelihood of dirt accumulation is more feasible. This work presents a first-of-its-kind dirt sample gathering strategy for the cleaning auditing robots by combining the geometrical feature extraction and swarm algorithms. This combined approach generates an efficient optimal path covering all the identified dirt locations for efficient cleaning auditing. Besides being the foundational effort for cleaning audit, a path planning approach considering the geometric signatures that contribute to the dirt accumulation of a region has not been device so far. The proposed approach is validated systematically through experiment trials. The geometrical feature extraction-based dirt location identification method successfully identified dirt accumulated locations in our post-cleaning analysis as part of the experiment trials. The path generation strategies are validated in a real-world environment using an in-house developed cleaning auditing robot BELUGA. From the experiments conducted, the ant colony optimization algorithm generated the best cleaning auditing path with less travel distance, exploration time, and energy usage.

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Vision System for Quality Assessment of Robotic Cleaning of Fish Processing Plants Using CNN

Cited by 17 publications

References 28 publications

A Comparative Review of Hand-Eye Calibration Techniques for Vision Guided Robots

A Comparative Review of Hand-Eye Calibration Techniques for Vision Guided Robots

Automatic System for Visual Detection of Dirt Buildup on Conveyor Belts Using Convolutional Neural Networks

A Novel Path Planning Strategy for a Cleaning Audit Robot Using Geometrical Features and Swarm Algorithms

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