Real-Time Explainable Multiclass Object Detection for Quality Assessment in 2-Dimensional Radiography Images

Abstract

Quality inspection and defect detection play a critical role in infrastructure safety and integrity specially when it comes to aging infrastructure mostly owned by governments around the world. One of the prevalent inspections performed in the industry is nondestructive testing (NDT) using radiography imaging. Growing demand, shortage of experts, diversity of required skills, and specific regional standards with a time-limited requirement of inspection results make automated inspection an urgent need. Therefore, utilizing artificial intelligence- (AI-) based tools as an assistive technology has become a trend for industrial applications, which automates repeated tasks and provides increased confidence before and during the inspection operation. Most of the works in quality assessment are focused on the classification of few categories of defects and mostly performed on public or noncomprehensive research datasets. In this work, a scalable, efficient, and real-time deep learning family of models for detection and classification of 10 various categories of weld characteristics on a real-world industrial dataset is presented. The models are evaluated and compared against each other, various critical hyperparameters and components are optimized, and local explainability of models is discussed. Additionally, AutoAugment for object detection and various techniques are utilized and investigated. The best performance for object detection and classification for 10 class models is reached by mean average precision of 72.4% and top-1 accuracy of 90.2%, respectively. Also, the fastest object detection model is able to evaluate a full 15360  $\times$  1024 pixels weld image in 0.39 seconds. Finally, the proposed models are deployable on edge-devices to perform as assistant to NDT experts or auditing professionals.