Classification of Defects in Bushes Using Deep Learning Approaches

I. S. Kushagra, R. Rakshith, Shubham Toraskar, Rahul J. Gujaran, C. S. Asha

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Bush manufacturing industries generate thousands of bushes every day. The quality of the product is very crucial; if not it often leads to a huge loss for the manufacturer. The bushes are made up of steel material, highly reflective, and cylindrical shapes. Typically, the bushes have several types of defects that include cracks on the surface, unfinished surfaces, dent, etc. The manual inspection is carried out to check the product quality which is very tedious and time-consuming. Hence, we address the necessity for automated inspection of these bushes for classification of defect and non-defect. The conventional methods often fail to automate the process because the size of the crack is minute, or the algorithms are not efficient. In this paper, we aim to solve the problem using a deep learning approach. A combination of multiple cameras are employed to collect the sample dataset, and convolutional neural network is employed for binary and multi-class classification of the defect types. The proposed method performed better with the accuracy of 99.85% for binary classification and 89.32% for multiclass classification for the test data. In addition, we compare the accuracy with state-of-the-art deep learning techniques.

Original languageEnglish
Title of host publicationSmart Sensors Measurements and Instrumentation - Select Proceedings of CISCON 2020
EditorsSanthosh K V, K. Guruprasad Rao
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages9
ISBN (Print)9789811603358
Publication statusPublished - 2021

Publication series

NameLecture Notes in Electrical Engineering
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering


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