Custom Object Detection System Development

Have you found that an off-the-shelf detection model fails to handle your objects? We develop custom object detection systems turnkey: from dataset collection to deployment on edge devices. With over 5 years of experience, we have delivered projects for retail, manufacturing, and security. In this article, we discuss how to choose the right architecture, fine-tune effectively, and achieve real-time performance. Typical tasks include counting products on shelves, inspecting defects on conveyor belts, and recognizing vehicles in parking lots. Often clients come with requests like 'teach a neural network to find defects' or 'count the number of products.' We help formulate the technical requirements, select the optimal model, and implement the solution.

How to choose the right detector for your task?

YOLOv8/YOLO11 is the optimal choice for most tasks. The Ultralytics implementation offers excellent documentation, active support, and built-in export to TensorRT/ONNX. For standard scenarios (1–20 classes, real-time), this is the starting point.

RT-DETR (Real-Time Detection Transformer) is a transformer-based detector that provides better quality at comparable speed to YOLOv8. Its architecture builds on DETR with acceleration via query selection. We recommend it when maximum mAP is needed and latency requirements are not extremely strict (74 FPS on T4).

Grounding DINO enables open-vocabulary detection: it finds objects based on textual descriptions without fine-tuning. Useful for prototyping and tasks with rare categories or frequently changing product lines. No dataset collection required—just formulate a query.

Table data from Ultralytics and Baidu Research

Why is fine-tuning on custom data critical?

Pre-trained detectors on COCO can recognize 80 classes. If your objects are not in this list, fine-tuning is necessary. Even if the classes are present, the domain may differ (night shots, specific angles), reducing quality. Fine-tuning adapts the model to your domain.

from ultralytics import YOLO

model = YOLO('yolov8l.pt')
results = model.train(
    data='dataset.yaml',
    epochs=100,
    imgsz=640,
    batch=16,
    optimizer='AdamW',
    lr0=0.001,
    lrf=0.01,
    weight_decay=0.0005,
    augment=True,
    degrees=10.0,
    mosaic=1.0,
    device=0
)

Structure of dataset.yaml:

path: /data/myproject
train: images/train
val: images/val
test: images/test

nc: 5
names: ['cat', 'dog', 'car', 'person', 'bicycle']

Augmentation for detection

Detection requires specific augmentations that must correctly apply to bounding boxes (a key part of bounding box augmentation):

• Mosaic — stitching 4 images into one, increasing context diversity
• MixUp — mixing two images with weights
• Copy-Paste — cutting objects and pasting them into a new context
• Random crop while preserving objects in the frame
• Albumentations: HorizontalFlip, RandomBrightnessContrast, GaussNoise

Metrics and post-processing

• [email protected] — mean Average Precision at IoU threshold 0.5
• [email protected]:0.95 — stricter: average mAP at IoU from 0.5 to 0.95 with step 0.05
• Precision / Recall at a specific confidence threshold
• FPS / latency — for real-time systems

Choosing the confidence threshold: use a precision-recall curve, select the threshold based on the acceptable balance for the specific application.

Non-Maximum Suppression removes duplicate detections. Parameters: IoU threshold (0.45–0.7), confidence threshold (0.25–0.5). For densely packed objects, apply Soft-NMS or Class-Agnostic NMS.

Deployment to target device

TensorRT engine for NVIDIA GPUs: export from Ultralytics with a single command model.export(format='engine'). ONNX for CPU deployment. For Raspberry Pi / Jetson: YOLO11n in TFLite / ONNX Runtime. This is a typical TensorRT deployment scenario for edge devices.

Case study: Metal surface defect detection

For a manufacturing client, we deployed a YOLOv8m model on an NVIDIA Jetson Xavier NX to detect surface defects on metal parts. After fine-tuning on 2000 labeled images and applying mosaic and copy-paste augmentation, we achieved 98% recall at 30 FPS, reducing manual inspection time by 70%. The solution ran reliably in a dusty industrial environment over a 6-month pilot. The project cost $12,000 and the client saves $60,000 annually in quality control labor.

Timeline estimates

Typical mistakes and how to avoid them

• Insufficient data per class — leads to low recall. Solution: collect at least 500 images per class.
• Overfitting due to excess empty frames. Solution: balance empty and object-containing images.
• Wrong augmentation: e.g., cropping that removes objects. Solution: configure RandomCrop to preserve objects.
• Ignoring post-processing: NMS with a high threshold may remove correct detections. Solution: tune the threshold on a validation set.

Our project workflow

1. Analysis and requirements gathering: identify objects, shooting conditions, FPS requirements.
2. Dataset collection and annotation: using CVAT or Label Studio. Minimum 1000 images.
3. Architecture selection and baseline training: iterative improvement with augmentation and hyperparameter optimization.
4. Testing on real data: evaluate mAP, precision, recall, FPS.
5. Deployment: export to TensorRT/ONNX/TFLite, integrate into your system.
6. Post-deployment support: monitor quality, fine-tune when new classes appear.

What is included in our service

• Technical documentation on architecture and usage instructions.
• Training your team on working with the model.
• Source code and training configurations.
• Access to a server with the trained model (optional).
• Quality guarantee: if performance degrades after one month, we fine-tune at no extra cost.

Contact us to discuss your project. Get a consultation on model selection and timeline estimation — we'll show you how quickly we can achieve the required detection quality.