System Interface & Telemetry Viewport
System Narrative & Problem Statement
Standard computer vision models trained on horizontal ground datasets perform poorly on aerial drone perspectives due to extreme perspective distortion, dense object clustering, and sub-30px target scales. AerialEye was engineered specifically to solve high-altitude disaster detection.
Upgraded to YOLOv11-Nano for optimized feature extraction, the model integrates SAHI (Slicing Aided Hyper Inference) to dynamically tile high-resolution aerial frames into 640x640 overlapping patches during inference, preserving critical pixel density for distant survivors and structural damage.
The model is trained on a unified, curated dataset of 6,327 high-altitude frames across 6 critical disaster classes: human, sos, vehicle, flood, road_damage, and crack. It achieves 89.4% mAP@0.5 and is exported to PyTorch (.pt), ONNX, and INT8 quantized TFLite for accelerated inference on low-power Google Coral Edge TPUs. Published openly on Hugging Face Hub.
Engineering Objectives
Key Engineering Highlights & Milestones
Published on Hugging Face Hub with 1,900+ downloads across weights and dataset
Curated unified dataset of 6,327 high-altitude aerial and disaster-response images
89.4% mAP@0.5 validation accuracy across 6 critical disaster classes
INT8 TFLite weight quantization tailored for low-power Google Coral Edge TPU hardware
System Architecture & Data Pipeline
Subsystem Technology Deep Dive & Implementation
6 Disaster Classes
Detects human, SOS markers, vehicles, flood zones, road breaches, and structural cracks in real-time.
SAHI Inference Slicing
Tiled sliding-window inference preserves fine pixel details for tiny objects viewed from high drone altitudes.
INT8 Quantization
Post-training INT8 quantization compressing model weights to run on low-power Google Coral Edge TPU hardware.
Hugging Face Hub
Open-access distribution of weights, ONNX pipelines, and training datasets for disaster relief researchers.
Implementation Code & Core Pipelines
1234567891011121314151617181920212223from sahi import AutoDetectionModel from sahi.predict import get_sliced_prediction class AerialEyeSlicer: def __init__(self, model_path="kilanisainikhil/AerialEye"): self.detection_model = AutoDetectionModel.from_pretrained( model_type="yolov11", model_path=model_path, confidence_threshold=0.35, device="cuda:0" ) def predict_high_altitude_frame(self, image_path: str): # 640x640 sliding window slices with 20% overlap result = get_sliced_prediction( image_path, self.detection_model, slice_height=640, slice_width=640, overlap_height_ratio=0.2, overlap_width_ratio=0.2 ) return result.object_prediction_list
Engineering Challenges & Technical Breakthroughs
Sub-30px Target Detection from High-Altitude Perspectives
Standard downsampling (e.g. resizing 4K drone video directly to 640x640) squashes distant humans and SOS markers into unidentifiable 4-pixel artifacts.
Integrated SAHI dynamic tile slicing to preserve original high-resolution pixel density across overlapping 640x640 windows, followed by unified non-maximum suppression (NMS).
Boosted small-object detection accuracy by +35.2% mAP@0.5 over monolithic standard detection.
Real-Time Edge Inference on Low-Power Drone Companion Computers
Full-precision float32 models exceed the compute and thermal limits of low-power drone payload hardware.
Engineered a calibrated post-training INT8 quantization pipeline exporting to Google Coral Edge TPU and TFLite runtimes.
Delivers 45+ FPS real-time detection on low-power 2-watt Edge TPU co-processors.
Performance Benchmarks & Efficiency Gains
| Metric / Criterion | Standard Baseline | Optimized System | Net Improvement |
|---|---|---|---|
| Validation mAP@0.5 | 54.2% (Standard YOLO Ground) | 89.4% (AerialEye + SAHI) | +35.2% mAP |
| Sub-30px Small Object Recall | 31.0% | 82.5% | +51.5% Recall |
| Coral TPU FPS | 12 FPS (Float32 CPU) | 48.2 FPS (INT8 Edge TPU) | 4.0x Speedup |
| Community Hub Transfers | 0 (Private) | 1,900+ (Hugging Face) | Global Impact |
Verified GitHub Commits & Release History
Engineering Arsenal & Technologies
Sleep Health Biometrics & Analytics
Statistical Biometric Modeling & Lifestyle Sleep Health Analysis