Question 1

What is the difference between image classification and object detection?

Accepted Answer

Image classification assigns a single label to an entire image (e.g., 'forklift'). Object detection identifies individual objects within the image, predicts their coordinates, and labels them (e.g., 'forklift at coordinates X, Y'). This allows you to count items, locate positions, and analyze spatial relationships between multiple objects in the frame.

Question 2

Which models do you use for object detection?

Accepted Answer

We primarily work with the YOLO (You Only Look Once) family, particularly YOLOv8 and YOLOv9, due to their incredible speed and accuracy in real-time applications. For complex tasks requiring maximum precision where latency is less critical, we utilize Faster R-CNN or Mask R-CNN. We select the model based on your hardware.

Question 3

Can object detection models run on low-power edge devices?

Accepted Answer

Yes. We specialize in optimizing models for edge hardware such as NVIDIA Jetson, Raspberry Pi, and industrial smart cameras. Using quantization and compilation frameworks like OpenVINO, ONNX, and TensorRT, we reduce model size and resource usage, allowing real-time inference (30+ frames per second) directly on local hardware without cloud latency.

Question 4

How much training data is required to detect a custom object?

Accepted Answer

For a simple object with consistent appearance, a few hundred annotated images can suffice. For complex objects under varying lighting, angles, and backgrounds, we typically recommend 1,000 to 5,000 labeled instances per class. We also use data augmentation and synthetic image generation to expand your dataset artificially.

Question 5

How do you handle overlapping or partially hidden objects?

Accepted Answer

We handle occlusions by training models on datasets that intentionally contain partially hidden objects. We also configure advanced non-maximum suppression (NMS) thresholds during inference, which prevents duplicate boxes for overlapping items. For video feeds, we add tracking algorithms that maintain an object's ID even when it is temporarily blocked.

Question 6

What is the difference between 2D and 3D object detection?

Accepted Answer

2D object detection estimates bounding boxes in flat pixel coordinates (X, Y, width, height). 3D object detection predicts a 3D bounding box (X, Y, Z, depth, width, height, and rotation) in physical space. We deploy 3D detection for autonomous vehicles, robotics, and spatial layouts where depth perception is essential.

Question 7

Can your systems detect objects in low-light or bad weather?

Accepted Answer

Yes, by utilizing specialized camera hardware (like infrared, thermal, or HDR sensors) and training models on diverse weather conditions. We also apply image enhancement pre-processing steps, such as contrast adjustment and noise reduction, to improve visibility and help the model identify targets in challenging environmental conditions.

Question 8

How long does it take to deploy a custom object detection system?

Accepted Answer

A pilot system using pre-labeled general classes takes 3 to 5 weeks. Designing, annotating, training, and optimizing a custom model for specialized industrial parts or proprietary packaging takes 8 to 12 weeks. This includes extensive onsite testing and calibration for camera angles and lighting setups.

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