A Steel Surface Defect Recognition Algorithm Based on Improved Deep Learning Network Model Using Feature Visualization and Quality Evaluation

Periodic inspection of false ceilings is mandatory to ensure building and human safety. Generally, false ceiling inspection includes identifying structural defects, degradation in Heating, Ventilation, and Air Conditioning (HVAC) systems, electrical wire damage, and pest infestation. Human-assisted false ceiling inspection is a laborious and risky task. This work presents a false ceiling deterioration detection and mapping framework using a deep-neural-network-based object detection algorithm and the teleoperated `Falcon’ robot. The object detection algorithm was trained with our custom false ceiling deterioration image dataset composed of four classes: structural defects (spalling, cracks, pitted surfaces, and water damage), degradation in HVAC systems (corrosion, molding, and pipe damage), electrical damage (frayed wires), and infestation (termites and rodents). The efficiency of the trained CNN algorithm and deterioration mapping was evaluated through various experiments and real-time field trials. The experimental results indicate that the deterioration detection and mapping results were accurate in a real false-ceiling environment and achieved an 89.53% detection accuracy.

Section: Experiments and Resultsmentioning

confidence: 99%

False Ceiling Deterioration Detection and Mapping Using a Deep Learning Framework and the Teleoperated Reconfigurable ‘Falcon’ Robot

Semwal

Elara

Melvin

et al. 2021

“…Studies related to object recognition and classification include the use of AlexNet [17] to recognize defects in dyed fibers or fabrics [4] and the use of VGGNet [18] based model to classify defects on the surface of steel [19]. In addition, a study [6] performed defect detection using sliding window methods to distinguish poor surface conditions (such as scratches and poor junctions).…”

Section: Product Inspection With Deep Learningmentioning

confidence: 99%

“…In addition, there are papers that help researchers apply deep learning by creating open datasets. Teh NEU surface defect database is a steel plate defect inspection dataset opened by [32] and the aforementioned papers [19,20] also used the dataset. Moreover, KolektorSDD(Kolektor Surface-Defect Dataset) was created by [33] and PCB scans dataset, which are laser scans of PCBs, were generated by [28].…”

Section: Product Inspection With Deep Learningmentioning

confidence: 99%

Product Inspection Methodology via Deep Learning: An Overview

Kim

Cho

2021

In this study, we present a framework for product quality inspection based on deep learning techniques. First, we categorize several deep learning models that can be applied to product inspection systems. In addition, we explain the steps for building a deep-learning-based inspection system in detail. Second, we address connection schemes that efficiently link deep learning models to product inspection systems. Finally, we propose an effective method that can maintain and enhance a product inspection system according to improvement goals of the existing product inspection systems. The proposed system is observed to possess good system maintenance and stability owing to the proposed methods. All the proposed methods are integrated into a unified framework and we provide detailed explanations of each proposed method. In order to verify the effectiveness of the proposed system, we compare and analyze the performance of the methods in various test scenarios. We expect that our study will provide useful guidelines to readers who desire to implement deep-learning-based systems for product inspection.

“…Then, they used the Dense Convolutional Network (DenseNet) [ 35 ] as the classifier model to obtain a better result of surface defect classification with manipulated data. Guan et al [ 36 ] proposed a novel recognition algorithm for steel surface defects. They used VGG19 as a pre-training model for the steel surface defect classification task, and established a DeVGG19 model to extract feature images in different layers from the defect weight model.…”

Section: Literature Reviewmentioning

confidence: 99%

SurfNetv2: An Improved Real-Time SurfNet and Its Applications to Defect Recognition of Calcium Silicate Boards

Tsai

Chen

2020

This paper presents an improved Convolutional Neural Network (CNN) architecture to recognize surface defects of the Calcium Silicate Board (CSB) using visual image information based on a deep learning approach. The proposed CNN architecture is inspired by the existing SurfNet architecture and is named SurfNetv2, which comprises a feature extraction module and a surface defect recognition module. The output of the system is the recognized defect category on the surface of the CSB. In the collection of the training dataset, we manually captured the defect images presented on the surface of the CSB samples. Then, we divided these defect images into four categories, which are crash, dirty, uneven, and normal. In the training stage, the proposed SurfNetv2 is trained through an end-to-end supervised learning method, so that the CNN model learns how to recognize surface defects of the CSB only through the RGB image information. Experimental results show that the proposed SurfNetv2 outperforms five state-of-the-art methods and achieves a high recognition accuracy of 99.90% and 99.75% in our private CSB dataset and the public Northeastern University (NEU) dataset, respectively. Moreover, the proposed SurfNetv2 model achieves a real-time computing speed of about 199.38 fps when processing images with a resolution of 128 × 128 pixels. Therefore, the proposed CNN model has great potential for real-time automatic surface defect recognition applications.