Frequently Asked Questions

An image processing system is a technology that automatically performs tasks such as object detection, quality control, counting and classification by analyzing images obtained from cameras using computer algorithms. It is used in factories to increase speed and accuracy instead of manual inspection.

Image processing in factories is used to control product quality on production lines, detect defective products, count objects, verify packaging accuracy and monitor production. Data is collected at every point where cameras are placed, AI models are trained and real-time analysis is performed.

In AI-powered quality control, models trained on camera images automatically detect product defects, color differences, shape deformities and missing parts. By learning from thousands of examples, the model can find errors that the human eye might miss with high accuracy.

Yes, most existing IP cameras are compatible with image processing systems. As long as camera resolution, fps speed and lighting conditions are sufficient, the system can be installed without additional camera purchases. Which cameras can be used is evaluated during field analysis.

Project duration varies from 2 weeks to 3 months depending on complexity. A simple object counting system can be completed in 2-4 weeks, while a multi-camera integrated quality control system can be completed in 6-12 weeks. Field analysis and pilot application are done in the first phase.

Yes, edge-based systems such as image processing and object detection can work without an internet connection. Data is processed on a local server. However, internet connection is required for remote monitoring, reporting and notifications.

Yes, integration with PLC can be established via Modbus TCP, OPC UA or custom protocols. Automatic actions such as stopping machines, starting, alarming or changing parameters on the PLC can be taken based on detected conditions.

Yes, since each business has different production lines and products, a custom dataset needs to be created. Piksel Analitik manages field image collection, data labeling and model training processes. The more data collected, the more accurate the model works.

Camera selection is made according to the distance to be monitored, area width, lighting conditions, speed requirements and budget. In industrial environments, IP cameras are generally preferred, and machine vision cameras in narrow spaces. The most suitable camera model is determined during field analysis.

False positive and false negative rates are reduced through model training. The model tested in the pilot phase is continuously improved with field data. A secondary control mechanism can also be established for human verification.

Yes, integration with existing ERP software can be done via API, database connection or file transfer. Data exchange can be provided with SAP, ERPNext, Opera or custom ERP systems.

Piksel Analitik serves throughout Turkey. Based in Gaziantep, projects have been or can be realized in all cities with industrial density including Istanbul, Ankara, Bursa, Kocaeli, Izmir, Konya, Kayseri, Adana, Manisa and Tekirdağ.

Yes, a pilot application is done first in most projects. A pilot project starting at a single camera point or a single production line can be expanded based on results. This reduces investment risk.

For a quote, information such as company details, process to be monitored, number and location of cameras, existing system infrastructure, budget range and project urgency is helpful. A clear problem definition ensures you get the most accurate quote.

Yes, monitoring, maintenance and continuous improvement support is provided after system installation. Technical support continues for model updates, adding new scenarios, hardware issues and software improvements.