Moreover, unlike destructive testing methods, the NDT solutions seldom affect the sample under examination [14]. Monitoring and keeping track of the weld defect during video analysis is a difficult task, which begs the question of artificial intelligence (AI)-driven quality evaluation of the materials. Numerous frameworks have been proposed where imaging modalities with the help of intelligent methods offer automatic evaluation of the weld quality and defect detection [15; 16; 17; 18; 19; 20; 21]. The recent development in the field of NDT of weld defects using traditional machine learning (ML) and deep learning (DL) architectures has spanned through performance analysis to classification and detection to semantic segmentation to process monitoring and many more [22; 23; 24; 25; 26; 27]. [PERSON] et al. proposed a multilevel-feature-input-based deep neural network (DNN) for multiclass mechanical defect classification (porosity, crack, etc.) [28] on radiographic images, where the hidden layers of the DNN extract 11 different geometric and intensity contrast features of background and weld defect that eventually reach the last hidden layer for defect class prediction. The authors improved the detection accuracy by incorporating the fine-tuning of a stacked autoencoder as pretrained network and achieved an accuracy of 91.4%. A transfer-learning-based weld classification framework on texture and geometric features over the X-ray images is presented by [PERSON] et al. [29] for classifying different types of defects such as the porosity and lack of penetration. Due to the small-scale data availability, the authors performed data augmentation and tested several pretrained architectures including AlexNet, VGG-16, VGG-19, ResNet-50, ResNet-101, and GoogLeNet. All the networks achieved impressive accuracy, with ResNet-50 and ResNet-101 being top performers with an accuracy equal to 100%. A semantic-segmentation-based three-stage weld classification method using DL on radiographic images is proposed by [PERSON] et al. [30] for visual defect classification including porosity and cracks. The first phase is concerned with the weld defect screening on radiographic images using deep belief network (DBN), the second phase is responsible for cylindrical projection of the defected parts to ease the segmentation, while the last stage employs the SegNet architecture for the segmentation task. The adopted network requires no preprocessing and reaches an accuracy of 98.6%. The studies referred to above consider the classification of mechanical defects. In the present study, the authors focused on the analysis of weld joints used to connect different lithium-ion cells to build a battery pack for electric vehicles; in this specific application, it is crucial to ensure the quality, both in terms of mechanical and electrical properties, i.e., the electrical resistance of the weld joint. This electrical characteristic is usually measured manually. It is a laborious and time-consuming task that could not be serviceable in an industrial setup. Moreover, the mechanical defects are generally physical and visible so can be classified considering morphological and textural features, whereas the defective weld joints that carry resistance issues may not have any visible defects, thus the traditional diagnostic imaging methods cannot efficiently be used. In fact, static images taken after the weld do not contain enough information to detect an electrical defect, thus explaining the need for a different approach. IR thermography uses a thermal camera to record continuous temperature distribution profiles of the weld area of interest. Real-time monitoring of the weld pool, weld defect diagnosis, weld geometry determination, and autocorrection of welding parameters (when coupled with soft computing) are only a few of the applications of IR thermography in metal welding.