Computer Vision Applied to Management and Optimization Logistics Operations

Resumo

This paper proposes a computer vision system for active monitoring of logistics operations, focusing on detecting and mitigating risks, such as interactions between people and vehicles. The main objective is to increase safety in the operational environment. IP cameras with RTSP transmission are used. Images are processed remotely on a notebook connected to the Wi-Fi network, while visual alerts are generated locally on the identified target. The use of color-coded segmentation in HSV space allows dynamically defining regions of interest (ROIs), with real-time adjustments using trackbars. Segmented objects have their contours detected and receive blue ROIs with configurable offsets. Target classification as ”person” or ”vehicle” is performed by a Convolutional Neural Network (CNN) with the YOLOv8n model. The preprocessing of the image dataset used the transfer learning technique via Roboflow, using 1,500 images. Data argumentation was applied, generating a final dataset of 3,612 images. The system was implemented in Python, using OpenCV and integration with ESP32 via HTML requests, triggering an alert LED. The combination of color segmentation in HSV space and CNN offers an accurate, efficient, and low-cost solution compared to traditional methods.