Deep learning models are widely being employed for object detection due to their high
performance. However, the majority of applications that require object detection are …

Quality inspection and defect detection remain critical challenges across diverse industrial
applications. Driven by advancements in Deep Learning, Convolutional Neural Networks …

Automatic vision-based inspection systems have played a key role in product quality
assessment for decades through the segmentation, detection, and classification of defects …

Fabric defects detection plays a critical role in the quality control of textile manufacturing
industry. It is still a challenge to realize accurate fabric defects detection due to variations of …

This study concentrates on deep learning-based lightweight object detection models on
edge devices. Designing such lightweight object recognition models is more difficult than …

Fabric defect detection is the important step of ensuring the quality and price of textiles. In
order to make the automatic fabric defect detection system used in production sites, a cloud …

In this paper, we propose an original deep learning framework for the automated counting
and geolocation of palm trees from aerial images using convolutional neural networks. For …

This study aimed to develop deep learning models that automatically detect impacted
mesiodens on periapical radiographs of primary and mixed dentition using the YOLOv3 …

As manifestations of Industry 4.0. become visible across various applications, one key and
opportune area of development are quality inspection processes and defect detection. Over …

Iron deficiency in plants causes iron chlorosis which frequently occurs in soils that are
alkaline (pH greater than 7.0) and that contain lime. This deficiency turns affected plant …