Exploring Novel Techniques to Detect Aberration from Metal Surfaces in Automobile Industries

Mohapatra, Debaniranjan; Chakraborty, Amit; Shaw, Ankit Kumar

doi:10.1007/978-981-33-4866-0_5

Cited by 1 publication

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“…VGGNet is very useful in object detection and classification of unseen objects as it is capable of good feature extraction. Idea behind the introduction of VGG is to enhance the classification accuracy by increasing the depth of CNNs [13,14]. Five max pooling filters having been embedded with CNN model in this work to down sample the input information.…”

Section: Proposed Methodologiesmentioning

confidence: 99%

Automated Visual Inspecting System for Fruit Quality Estimation Using Deep Learning

Mohapatra

Das

Mohanty

et al. 2021

Lecture Notes in Electrical Engineering

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Automated visual inspection using deep learning is widely used in recent years. In the field of agriculture, deep learning can be deployed to reduce effective man power, best time utilization, and supreme classification with improved accuracy. In agriculture, DL can be imported in many applications like soil identification, disease classification, fruit grading, and many more. Fruit quality classification is an essential part in farming as it implies to the return directly. Hence, an automated system is much needed to improve the classification of fruits with high accuracy and less time. In this paper, three different CNN models are proposed, namely simple CNN, ResNet50, and VGG19 for the said purpose. A comparison between CNN and two other forms of CNN (ResNet50 and VGG19) is presented in terms of performance. This work has achieved the optimum result with grading accuracy over 94% using VGG19 model.

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Section: Proposed Methodologiesmentioning

confidence: 99%