Masood Aslam

Automated leather defect classification has gained a lot of attention in recent years with the advancement of automation in the leather industry. In recent years a plethora of new techniques have been presented which aim to improve the automated defect classification pipeline. However, not much attention is given to the important task of automated defect detection along with categorization. The aim of this work is to evaluate the current state object detectors and their systematically adapted variants for the task of leather defect detection. A major goal of this study is to provide recommendations for model identification/selection and design of new detection methods for the task of leather defect detection. Important findings of this study are that multistage detectors better generalize to difficult defects of varying characteristics. Also, shallower backbones were found to be more accurate and are well-suited to problems where a relatively limited amount of data is available for training.

Automated leather defect classification has gained a lot of attention in recent years with the advancement of automation in the leather industry. In recent years a plethora of new techniques have been presented which aim to improve the automated defect classification pipeline. However, not much attention is given to the important task of automated defect detection along with categorization. The aim of this work is to evaluate the current state object detectors and their systematically adapted variants for the task of leather defect detection. A major goal of this study is to provide recommendations for model identification/selection and design of new detection methods for the task of leather defect detection. Important findings of this study are that multistage detectors better generalize to difficult defects of varying characteristics. Also, shallower backbones were found to be more accurate and are well-suited to problems where a relatively limited amount of data is available for training.

Security and safety is a big concern for today’s modern world. For a country to be economically strong, it must ensure a safe and secure environment for investors and tourists. Having said that, Closed Circuit Television (CCTV) cameras are being used for surveillance and to monitor activities i.e. robberies but these cameras still require human supervision and intervention. We need a system that can automatically detect these illegal activities. Despite state-of-the-art deep learning algorithms, fast processing hardware, and advanced CCTV cameras, weapon detection in real-time is still a serious challenge. Observing angle differences, occlusions by the carrier of the firearm and persons around it further enhances the difficulty of the challenge. This work focuses on providing a secure place using CCTV footage as a source to detect harmful weapons by applying the state of the art open-source deep learning algorithms. We have implemented binary classification assuming pistol class as ...

With many applications in security, surveillance, and intelligent transportation systems, Automatic License Plate Recognition (ALPR) is a fundamental and important task. In the recent era with the growth of Deep Learning (DL) techniques, ALPR also has shown dramatically accurate results as other computer vision problems. Most of the practical ALPR systems are country-specific, for example, a lot of work has been done for Chinese, Indian, Brazilian, American, or European vehicle plates that have specific vehicle plate format. This work focuses on vehicle plate recognition for multiple formats implemented in the same country or state. For this purpose, Pakistan has been chosen where license plates have unique formatting styles even within different provinces that enhance the complexity of character segmentation and recognition, and ultimately make it a challenging problem. Moreover, due to commercialization and privacy concerns, there was no publicly available dataset of Pakistani lic...

As part of industrial quality control in the leather industry, it is important to identify the abnormal features in wet-blue leather samples. Manual inspection of leather samples is the current norm in industrial settings. To comply with the current industrial standards that advocate large-scale automation, visual inspection based leather processing is imperative. Visual inspection of irregular surfaces is a challenging problem as the characteristics of the abnormalities can take a variety of shape and color variations. The aim of this work is to automatically categorize leather images into normal or abnormal by visual analysis of the surfaces. To achieve this aim, a deep learning based approach is devised that learns to recognize regular and irregular leather surfaces and categorize leather images on its basis. To this end, we propose an ensemble of multiple convolutional neural networks for classifying leather images. The proposed ensemble network exhibited competitive performance...