employee
Russian Federation
employee
Russian Federation
student
Russian Federation
VAK Russia 1.2.2
VAK Russia 2.3.1
UDC 656.073
Binary image classification tasks are widely employed in engineering and manufacturing systems, including automated control, machine vision, and object monitoring. As the complexity of imaging conditions increases and data volumes expand, it becomes essential to evaluate classical computer vision algorithms alongside deep learning neural network methods to identify the most effective method. Purpose: to perform a practical comparison of the effectiveness of a traditional image processing algorithm and the YOLO neural network model for addressing the binary classification challenge. Methods: traditional image processing techniques, including threshold filtering, morphological operations, and geometric feature analysis, as well as the YOLO detection model trained on a labeled dataset. Results: the classical algorithm has demonstrated high processing speed and adequate accuracy under stable lighting conditions; however, it has exhibited a pronounced decline in performance when imaging conditions changed. In contrast, the YOLO model has demonstrated enhanced accuracy, resilience to both photometric and geometric variations, and consistent performance even in the presence of extraneous noise. Practical significance: the results can guide the development of computer vision systems, aid in the selection of the optimal algorithm for specific operational scenarios, and facilitate the creation of hybrid systems that combine the strengths of both traditional and neural network methods. Discussion: this study confirms that while traditional methods are effective in resource-constrained environments, they are vulnerable to external influences. Conversely, neural network approaches offer superior generalization and stability, making them more advantageous in fluctuating imaging conditions. The novelty of the research lies in the comparative analysis of these methods under identical processing parameters, with a particular focus on practical indicators. This approach facilitates an objective assessment of the applicability domain inherent to each method.
computer vision, deep learning, YOLO, binary classification, image analysis, image processing, neural network methods
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