International Journal of Electrical and Computer Engineering (IJECE) Vol.8, No.6, December2018, pp. 5032~5040 ISSN: 2088-8708, DOI: 10.11591/ijece.v8i6.pp5032-5040  5032 Journal homepage: http://iaescore.com/journals/index.php/IJECE Shape and Level Bottles Detection Using Local Standard Deviation and Hough Transform Nor Nabilah Syazana Abdul Rahman 1 , Norhashimah Mohd Saad 2 , Abdul Rahim Abdullah 3 1,2 Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka, Malaysia 2,3 Center for Robotics and Industrial Automation, Universiti Teknikal Malaysia Melaka, Malaysia 3 Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Malaysia Article Info ABSTRACT Article history: Received Feb 23, 2018 Revised Jul 19, 2018 Accepted Jul 22, 2018 This paper presents shape and level analysis using local standard deviation and Hough transform technique to detect the shape and level of the bottle. A 155 sample images are used as a test product to detect shape and level. Local standard deviation is used contrast gain technique to segment the shape of the bottle by enhancing the contrast of the image. The ratio of the area is calculated from the extent parameter. The maximum and minimum water level is created by using Hough transform technique to identify the level of the water. Decision tree is applied to classify the shape and level of the bottle either good or defect condition. From experimental result, 97% and 93% accuracy of shape and level is achieved which shows that the proposed analysis technique is potential to be applied for beverages product inspection system. Keyword: Automated inspection Hough transform Level detection Local standard deviation Shape detection Copyright © 2018Institute of Advanced Engineering and Science. All rights reserved. Corresponding Author: Norhashimah Mohd Saad, Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia. Email: norhashimah@utem.edu.my 1. INTRODUCTION Industries such as textile, semiconductor, food and beverages have agreed that a system based approach is more efficient and sustainable to be applied in manufacturing process [1]. Visual inspection is categorized into two, which are manual inspection and automatic inspection. Manual inspection is commonly used human as inspector for product quality [2]. The study made by [3] have shown the manual inspection performed by human operator is inefficient and consume more time due to working condition. The task given by industry is repetitive and difficult to do if no proper training is conducted. An automatic inspection seems to be better compared to manual inspection. For these reason, the application of an automated inspection system for detection and classification of defect is desirable [4]. The application of an automated inspection has reduced execution time, computational cost and high percentage error free [5]. Some reviews are made from previous research to compare the inspection system technique. Researcher in [6] proposed shape detection using a partial erosion-based technique to automate the shape segmentation process of plastic bottle. The proposed technique involved morphological and erosion process to segment the bottle shape and extract the features from the segmented image. The accuracy of the proposed technique is more than 80%. However, the partial erosion-based technique is inefficient and give bad effect to the image when the partial erosion is set less than 50% or more than 100%. The statistical histogram based Fuzzy C-means (SHFCM) is suggested by [3] to identify the apple defect for fruit quality inspection. SHFCM is used to classify the healthy and defect apple. The experimental result shows the accuracy achieved by healthy apple is 96% and defect apple is 91%. Nevertheless, SHFCM is the combination of the