REAL-TIME DETECTION OF DEFECTIVE PRODUCTS IN A PRODUCTION LINE USING TENSORFLOW OBJECT DETECTION API AND OPENCV

Abstract

Object detection is widely employed in various applications, including autonomous vehicles, scanning digital images, street traffic detection, object classification, and facial detection. Object detection does more than just find and classify things in an image. It also finds where those things are and makes bounding boxes around them. Finding every instance of an object from a given class, such as people, cars, or faces in a picture, is the aim of object detection. Even though there are often few instances of the object in the photograph, there are a vast array of locations and scales where it could appear that must be investigated. As a result, most effective object detection networks combine object identification methods with picture classifiers based on neural networks. We are able to develop, train, and deploy object identification models using the Tensorflow Object Detection API, an open-source platform built on Google's TensorFlow, and a Python library termed OpenCV that allows anyone to perform specific computer vision through trained image processing. The thesis mainly focuses on the real-time detection of defective products in a production line. For this, we need a well-trained object detection model. For our thesis, we used SSD-MobileNet-v2, which is a model that has already been trained on the COCO (Common Objects in Context) dataset. But this model cannot detect our target classes; therefore, we collected training samples and fine-tuned the model for better prediction. We fine-tuned the model for 2500, 5000, and 10000 steps. With increases in the training steps, performance metrics such as mAP (Mean Average Precision) and recall increase. Hence, the fine-tuned model that has been trained at 10,000 steps showed better overall performance. It showed a mAP value of 0.9278 and a recall value of 0.9379.