High-level API design for an Object Detection service that includes counting of object classes (e.g., counting cars, persons, animals, etc. in an image or video frame). This design follows a pattern similar to anomaly detection, sensor fault detection, etc., but now focuses on computer vision tasks—training, detecting, and retrieving detections/counts from images or video frames.

1. Overview

1. Purpose
   • Ingest images or video frames and detect objects of various classes.
   • Provide the counts of each class detected.
   • Allow training or retraining of detection models (e.g., YOLO, Faster R-CNN, or custom CNN models).
   • Support retrieval of detected objects, bounding boxes, and confidence scores.
2. Data Flow
   1. Data Ingestion: The system receives labeled or unlabeled images/videos.
   2. Model Training: Train object detection models on labeled data (bounding boxes, classes).
   3. Object Detection: For new images or frames, detect objects, generate bounding boxes, classify object types, and produce counts.
   4. Result Retrieval: Clients can retrieve the detection results, including bounding boxes, classes, confidence scores, and aggregated counts per class.

2. Common Object Detection Algorithms

1. Faster R-CNN
   • Two-stage detector (region proposal + classification).
   • Often high accuracy, but can be slower than single-stage methods.
2. YOLO (You Only Look Once)
   • Single-stage detector that’s often very fast.
   • Popular variants: YOLOv3, YOLOv5, YOLOv7, etc.
3. SSD (Single Shot MultiBox Detector)
   • Single-stage detector focusing on speed with decent accuracy.
4. RetinaNet
   • Single-stage detector with Focal Loss to address class imbalance.
5. Transformer-based Approaches (e.g., DETR)
   • Uses attention mechanisms to detect objects with fewer hand-crafted components.

3. API Endpoints

3.1. Image/Video Ingestion (Optional)

Depending on your system architecture, you might store images/videos in a dedicated storage layer (S3, local file system, etc.) rather than sending them directly through the API. If you prefer to ingest them via the API:

Endpoint:

POST /api/v1/object-detection/data

Description:

• Upload images or frames (optionally with labels if you have ground truth).