Abstract: Deep Learning methods, particularly Convolutional Neural Networks (CNN), have become widely adopted in indus- trial computer vision applications due to their strong image classification capabilities. In industrial environments, reliable object recognition is essential for automated systems such as warehouse automation and sorting processes, where incorrect classifications may lead to operational failures and reduced system reliability. Despite their performance, CNN-based models remain vulnerable to adversarial perturbations, raising concerns regarding their deployment in safety-critical applications. Industrial vision systems deployed in safety-critical environ- ments must operate reliably under adversarial perturbations and common perception degradations. This work presents a robustness analysis of image classification models for industrial environments using RGB and RGB-D data. An industrial object dataset containing seven classes was constructed through an ob- ject detection and extraction pipeline, resulting in object-centered image samples. Two CNN-based architectures were developed and evaluated: a baseline RGB model and a multimodal RGB-D model. Model robustness was analyzed through both formal verifica- tion and corruption-based robustness evaluation. Formal verifi- cation was performed using the alpha-beta-CROWN framework under different perturbation magnitudes, assessing whether the CNNs preserve their predictions under bounded perturbations. In addition, corruption-based experiments following the ImageNet- C protocol were conducted to evaluate model behavior under common image degradations.