Frequently Asked Question

Object detection is a computer vision technique that identifies and locates objects within an image or video, drawing bounding boxes around them.

YOLOv8, YOLO-NAS, Faster R-CNN, and SSD are widely used modern architectures for object detection.

Popular frameworks include TensorFlow, PyTorch, and Ultralytics YOLO, all supporting state-of-the-art object detection models.

The biggest cause of occupational accidents in Türkiye is humans and hazardous industrial machines operating in the same areas, combined with the inability to detect risks early enough in these environments.

High-quality, annotated datasets with realistic scenarios are essential for robust detection in occupational health and safety applications.

Trends include transformer-based models (e.g., DETR), self-supervised learning, edge AI deployment, and multimodal systems combining vision with IoT data.

These systems analyze human presence and hazardous zones in real time and automatically stop machine operation when a risk is detected.

Object detection enables automatic monitoring of hazardous areas, detection of personal protective equipment (PPE), and identification of unsafe behaviors, improving workplace safety.

AI systems analyze risky behaviors and provide concrete field data to organizations, enabling the development of a preventive safety culture.

Yes, object detection can reliably detect helmets on workers, enabling real-time monitoring of helmet compliance.

Cameras with object detection identify and track personnel or vehicles entering restricted areas, triggering alerts when violations occur.

Yes, object detection can identify forklifts, workers, and obstacles in real-time, enabling collision avoidance systems.

Models are trained to recognize PPE items like helmets, vests, masks, and gloves, allowing automated compliance verification.

Yes, object detection can monitor the presence and placement of safety signs in workplaces.

Object detection can be trained to identify specific tools and flag unattended or misplaced equipment in sensitive areas.

Real-time object detection generally requires GPU acceleration, edge AI devices (e.g., NVIDIA Jetson, Intel Movidius), or high-performance CPUs for inference.

Edge devices, on-premise servers, and cloud-based platforms are standard deployment options depending on latency and privacy requirements.

No, it is not mandatory. Unlike traditional systems, ISEE-CAM uses Edge AI (Artificial Intelligence at the Edge) technology, which performs analysis directly on the device without the need to send images to an external cloud server. In this way, your data never leaves the factory, full data privacy is ensured, and real-time results are produced within milliseconds without being affected by internet interruptions.

No, ISEE-CAM does not only provide reporting; it produces real-time outputs the moment it detects a danger. Thanks to the digital outputs on the device, when a violation is detected, it can activate sirens, turn on warning lights, or instantly stop machine/forklift movement without the need for any external software. All these processes take place within milliseconds thanks to Edge AI.

No, ISEE-CAM is an independent system with its own processor. Since it performs all AI analyses on the camera itself (Edge AI), it does not require an external server, a high-performance computer, or licensed software. Simply mounting the camera and supplying power is sufficient for the system to operate.

Yes, you can also use our system with your existing camera infrastructure. If you already have an installed system, we can integrate all your cameras into a central AI server to perform both occupational safety and quality control analyses. Thus, while protecting your existing hardware investment, you can transform your facility into an AI-supported smart production center within seconds.

No. AI-powered cameras do not detect heat like thermal cameras. They use visual data (RGB images) to identify people, machines, movements, and hazardous areas. Through computer vision and deep learning algorithms, objects are analyzed based on shape, posture, movement, and context. Thermal cameras only detect heat differences and can produce misleading results due to ambient temperature, reflections, or weather conditions. AI-powered camera systems detect human presence and risky interactions more precisely and contextually without relying on heat, making them more reliable and versatile for industrial safety applications.

In many facilities, safety measures rely on fixed sensors. In dynamic production environments, these systems create blind spots, causing risks to be detected too late.

Sensors are fixed to specific points and cannot cover all risk scenarios under changing site conditions.

Yes, but performance depends on camera quality and dataset diversity. Infrared or thermal cameras improve results in low-light scenarios.

Common challenges are occlusion, varying lighting, camera placement, and the need for robust models resilient to real-world conditions.

Yes, ISEE-CAM is fully compatible with heavy industrial conditions. According to its technical specifications, it operates continuously in extreme temperatures between -20 °C and +70 °C.

Yes, ISEE-CAM is specially produced for such harsh conditions. Thanks to its IP69K certification, it operates at full performance in very dusty areas and outdoor environments.

Yes, object detection models trained on fire and smoke datasets can provide fast detection and early warnings.

Object detection locates people in camera feeds, enabling automated calculation of distances between individuals for compliance monitoring.

Object detection can be combined with pose estimation or activity recognition to identify falls, slips, or abnormal events.

Yes, with high-resolution cameras and specialized models, object detection can monitor hand positions to prevent injuries.

Models detect and count individuals entering or present in dangerous zones, supporting evacuation plans and access control.

It provides real-time situational awareness, detects the presence of people and hazards, and supports automated incident reporting.

Yes, it is possible. Thanks to its flexible AI architecture, ISEE-CAM can be retrained to recognize objects or equipment specific to your operation, in addition to standard objects (personnel, forklifts, etc.). With a dataset study conducted by our expert team, custom object definitions can be made for your system, and operation-specific tracking and inspection processes can be rapidly deployed.

Accuracy depends on the model, dataset quality, and environment. Models trained with relevant data and high-quality annotations perform better in specific industrial settings.

Regular model retraining with updated data, improved annotation quality, and use of advanced models reduce false positives.

Detection events can trigger alarms, notifications, or automated shutdowns via API integrations or IoT devices.

In quality control processes, precision can be adjusted directly according to the manufacturer’s needs and tolerance values. Beyond standard solutions, thanks to high-resolution cameras and lenses selected specifically for the project, it is possible to operate with high precision even at micrometer (micron) level details. The system’s operating principle is optimized with appropriate device selection to meet the most demanding quality criteria.

Yes, it is possible to keep historical records through the system. According to your needs, you can integrate a dedicated recording device into the system to store data on a daily, weekly, or monthly basis. In addition, if an internet connection is provided, you can direct the images from the analyzing cameras directly to your existing recording devices and keep the entire process recorded.

Yes, the image processing camera can be monitored in real time. You can either follow the system in real time via a monitor installed near the camera, or through a network interface if an internet connection is available. Thus, while AI analyses continue, you also have the opportunity to observe field operations live.

Yes, multiple cameras can communicate with each other and work in a coordinated manner. This does not require a high-speed internet connection; communication can be provided via a simple local network (switch connection) connecting the cameras. In this way, cameras can make joint decisions based on data received from each other and produce instant outputs accordingly, managing the safety of the entire site in a synchronized manner.

Data augmentation improves model robustness by exposing it to diverse lighting, angle, and scale conditions, increasing generalization performance.

Class imbalance reduces detection accuracy for rare objects and should be addressed using sampling and weighting strategies.

Samples with the highest model uncertainty are selected for labeling, improving training efficiency.

mAP summarizes model precision and recall performance across multiple IoU thresholds.

IoU measures the overlap between predicted and ground-truth bounding boxes to evaluate detection accuracy.

FPS defines how many frames per second the system can process and directly determines latency performance.

Yes, the system includes real-time status monitoring and fault notification mechanisms. In case the camera shuts down, loses connection, or encounters a hardware failure, you can receive instant alerts via dry contact outputs or signals sent over the network. Thus, you can intervene without any interruption in your safety and quality control processes.

In image processing systems, compliance with PDPL (Law on the Protection of Personal Data) is directly related to where and how the data is processed. ISEE-CAM uses Edge AI technology, which processes images directly on the camera without transferring them to a central server or the cloud. Thus, personal data (facial features, identity-defining details) never leave the device and are used only for instant analysis (such as personnel/forklift detection) without being recorded. Therefore, the risk of data leakage is eliminated and PDPL requirements are technically met at the highest security level.

Data is processed locally without cloud transmission, significantly reducing privacy risks.

Models should be periodically retrained depending on environmental and operational changes.

Changes in environment and data distribution cause performance degradation.

The hardware and software warranty of the system is 1 year. During the warranty period, in any technical situation that may occur, our authorized experts immediately contact the customer and provide the necessary support. A fast and solution-oriented technical support process is carried out to ensure that your operations are not disrupted, both in terms of the physical durability of the device and the continuity of the software.

In case a false positive is detected in the system, collecting the incorrect data, retraining the AI, and updating and reactivating the system are completed within one business day. Thanks to our agile software infrastructure and expert team, site-specific optimizations are carried out rapidly, ensuring uninterrupted operation of the system with the highest accuracy rate.

Yes, secure integration is possible via OPC-UA, Modbus TCP, or REST APIs.

Detected events are transferred to SCADA interfaces as alarms, logs, and statistics.

It will play a central role in autonomous systems, digital twins, and smart factories.

Combining visual, LiDAR, thermal, and audio data significantly increases perception accuracy.