This study introduces an innovative approach to automatically identify and tally faulty components within an Industry 4.0 manufacturing environment. The method involves integrating an affordable image sensor with a Raspberry Pi kit that operates AI-driven software. By examining images or video streams from the production line, this proposed solution swiftly identifies and categorizes defective items, regardless of their orientation to the camera. This automated process not only reduces human error but also seamlessly integrates with existing systems, enhancing workflow efficiency. This solution transforms the defect counting and recognition process in Industry 4.0 production lines, leading to elevated quality control standards and enhanced operational productivity. The achieved accuracy of our work is 95%, and our training dataset encompasses over 500 images.