Abstract—Inadequate solid waste management poses a threat to public health and environmental sustainability in urban contexts. This study aimed to determine how a computer vision system based on Open Source Computer Vision Library (OpenCV) reduces processing time and improves accuracy in solid waste classification in Piura, Peru. An applied study with a quantitative approach was conducted with operators. Data were collected using observation guides and a validated questionnaire. The results showed significant improvements: the number of waste items classified per minute increased by 80%, and the average processing time per batch was reduced by 46.8%. The system achieved an overall accuracy of 83.2%, with classification accuracy rates exceeding 70% in all evaluated categories. It is concluded that computer vision based on OpenCV effectively optimizes both speed and accuracy in solid waste classification, thereby significantly improving urban environmental management.
 
Keywords—artificial intelligence, sustainable development, public health, recycling, waste management
 
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