  The expanding applications, utilized by more users, enhance hardware
performance and further develop cloud systems for big data processing. This
leads to numerous unexplored deep learning applications, especially in advanced
computer vision for object recognition. Deep learning in image processing
encompasses varied tasks from recognizing elements with diverse shapes and
sizes to complex element classification, coping with varying backgrounds and
lighting conditions, and text recognition. Its advantages lie in robust setup
and high performance for recognizing complex elements. This work aims to
develop a deep learning-based detection system for automated recognition of
assembly components differing in geometry, size, contour, or color.
Implementing the YOLOv4 algorithm, the system detects components based on their
characteristics. Testing with 13 components involves capturing them in
different orientations, numbers, individual parts, or assembled groups using a
Raspberry Pi microcontroller and camera. Evaluation focuses on correct object
recognition, confidence values, different compositions, distances between
objects, and environmental factors affecting system quality. Results show
positive object recognition across all scenarios, irrespective of orientation
or number of objects. Even densely packed objects are correctly recognized with
high confidence (97-100%). Lighting conditions don't significantly impact
results, and all objects are properly labeled. The developed system is suitable
for real-time two-dimensional component detection, with potential for extension
to three-dimensional analysis using multiple cameras with varied positioning
and views.
