Custom AI model for object detection
With the rapid advancements in AI and machine learning, custom AI models have become a pivotal technology for various applications. One such application is video-based object detection and counting, which has numerous practical uses, from quality control to traffic management, and manufacturing analytics.
While Camlytics pre-trained AI models can detect and count people, vehicles, faces, and classify them, there are a lot of businesses that need tailored video based detection and classification.
Understanding video-based object detection, counting and classification
Custom object detection involves identifying and locating objects within an image or video frame, while counting involves tallying the number of detected objects. This dual functionality is essential for applications that require real-time monitoring and analysis of dynamic environments.
If it's necessary to detect object class, we can also help with it. Camlytics AI can be trained to detect unlimited number of classes for custom objects (for example, classify different product types on a conveyor belt).
Steps to train a custom AI model
Data Collection
You provide us with a diverse dataset containing video footage relevant to the objects you aim to detect and count. Ensure the dataset covers various scenarios and object states to improve model robustness. The optimal size of the data set is 20+ hours of video footage containing objects of interest in different positions.
Data Annotation
Our team annotates the collected video frames with labels that identify objects of interest.
Model Selection
Our dev team chooses a suitable model architecture for object detection, such as YOLO, SSD, or Faster R-CNN. Consider the trade-offs between speed and accuracy.
Training the Model
Our team splits the annotated dataset into training, validation, and test sets. Uses a framework like TensorFlow or PyTorch to train the model. Implements data augmentation techniques to enhance model performance. Evaluates the model's performance on the validation set using metrics like precision, recall, and mean Average Precision (mAP). Fine-tunes the model by adjusting hyperparameters and retraining as necessary.
Deployment
Our teams deploys the trained model to a Camlytics Single platform for real-time video processing.
We pass you the installer and you test the model in your environment. We fix the false positives and false negatives issues that might occur.
The whole process between the data set provision and ready to deploy installer takes around 4-6 weeks.
Case study
One of our US clients has a fish farm where a reliable fish detection and counting was needed. There was a big variety of fish sizes and species. A reliable detection with multi fish tracking was requested.
We collected around 10 hours of video footage and trained a custom AI detector. The detector is used mostly for counting and has proved the accuracy to be around 99%
Conclusions
Custom AI model training for video-based object detection and counting holds immense potential across various industries. By leveraging advanced AI techniques, businesses can enhance operational efficiency, improve safety, and gain valuable insights from video data.
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