Bounding Box Object Detection
Tight-fit 2D bounding box labeling with occlusion tagging, truncation flags, and fine-grained taxonomy categorizations for computer vision AI.
Quality & Precision Benchmarks
MAP@0.5 PRECISION
96.8%
BOUNDING BOX TIGHTNESS
Sub-pixel Fit
QA AUDIT RATE
100% Double-Pass
EXPORT FORMATS
YOLO TXT, Pascal VOC XML
Dataset Taxonomy & Output Structure
bbox_2d (x_min, y_min, width, height)
occlusion_level (0: None, 1: Partial, 2: Heavy)
truncation_flag (Boolean Boundary Clip)
class_label (Taxonomy Latch)
Specific Type Tasks & Applications
- • Retail E-Commerce Shelf Product Detection
- • Industrial Defect Inspection
- • Security & Surveillance Object Tracking
What Is Right vs What Is Wrong
| COMMON COMPETITOR ERRORS (WRONG) | BLUE PROJECTS GROUND TRUTH (RIGHT) |
|---|---|
| FAIL: Loose 2D bounding boxes leaving large background margins | PASS: Tight-fit sub-pixel bounding box boundaries hugging exact object margins |
| FAIL: Missing occlusion and truncation metadata attributes | PASS: Explicit occlusion severity levels (0-2) and truncation clipping flags |
Files & Telemetry Data Example (Python)
import json
# Load Blue Projects Annotation Data Type: Bounding Box Object Detection
with open("bounding-box-object-detection_annotation_sample.json", "r") as f:
data = json.load(f)
print("Loaded Keys:", list(data.keys()))
Why Blue Projects for Bounding Box Object Detection?
Request a free POC pilot annotation batch (up to 500 frames annotated free within 24 hours).
Request Free POC Pilot Batch →