Industrial inspection is a crucial task to ensure the quality and safety of industrial processes. In recent years, deep learning models have shown promising results in performing these tasks. This thesis proposes a new deep learning architecture called YOLOX-Ray, specifically designed for industrial inspection. The YOLOX-Ray architecture is based on the family of You Only Look Once (YOLO) object detection algorithms and incorporates a new attention mechanism, SimAM, which allows for better feature extraction in the Feature Pyramid Network (FPN) and Path Aggregation Network (PAN). Additionally, a new cost function, Alpha-IoU, is implemented, which allows for better detection of objects on a smaller scale. The performance of the YOLOX-Ray architecture is evaluated in three case studies: hotspot detection, crack detection in infrastructure, and corrosion detection. Results show that the YOLOX-Ray architecture outperforms all other configurations, achieving mAP50 values of 89% for hotspot detection, 99.6% for crack detection, and 87.7% for corrosion detection. For the most challenging metric, mAP50:95, the values are 44.7%, 66.1%, and 51.8%, respectively. A comparative study analyzes the contribution of each component of the YOLOX-Ray architecture, showing that the combination of the SimAM attention mechanism and the Alpha-IoU cost function is essential to achieve the best performance. In summary, the proposed architecture’s ability to detect and locate multi-scale objects in industrial environments opens new possibilities for effective, efficient, and sustainable inspection processes in various industries, revolutionizing the field of industrial inspections.