YOLOv8 object detection model trained to detect Opuntia invasive species from Google Street View imagery using high-resolution sliced inference workflows.

This model is part of the StreetViewSentinel project, focused on large-scale environmental monitoring and invasive species detection through computer vision.

Model Description

The model was trained using:

YOLOv8
SAHI sliced inference
High-resolution Google Street View imagery
Custom preprocessing and smart tiling pipeline

The objective is to improve detection performance on large panoramic images where invasive species may occupy small regions of the scene.

Intended Use

This model is intended for:

Invasive species monitoring
Environmental analysis
Geospatial biodiversity studies
Large-scale Street View image analysis
Research applications

Training Data

Training data consists of manually annotated Google Street View imagery containing instances of Opuntia species.

Preprocessing included:

Vertical image splitting
Margin-based cropping
Smart tiling
Data augmentation

Inference

Example using Ultralytics:

python

from ultralytics import YOLO

model = YOLO("model.pt")

results = model.predict(
    source="image.jpg",
    conf=0.3
)

Example using SAHI sliced inference

python

from sahi import AutoDetectionModel
from sahi.predict import get_sliced_prediction

detection_model = AutoDetectionModel.from_pretrained(
    model_type="yolov8",
    model_path="model.pt",
    confidence_threshold=0.3,
    device="cpu"
)

result = get_sliced_prediction(
    "image.jpg",
    detection_model,
    slice_height=256,           # Best results obtained with
    slice_width=256,            # patches of 2000 by 2000
    overlap_height_ratio=0.2,
    overlap_width_ratio=0.2,
)

Evaluation Metrics

The model was first evaluated using standard object detection metrics on a held-out validation dataset.

Object Detection Metrics

Metric	Value
mAP50	XX
mAP50-95	XX
Precision	XX
Recall	XX

Classification-Oriented Evaluation

After object detection evaluation, the model was additionally evaluated on a separate test dataset using a binary image-level classification approach.

An image was considered positive if the detector produced at least one detection.

This evaluation strategy was designed to assess the model as a practical screening tool for detecting the presence of Opuntia invasive species in Google Street View imagery.

Classification Rule

text

Positive image  -> at least one detection
Negative image  -> no detections

Classification metrics

Metric	Value
Accuracy	XX
Precision	XX
Recall	XX

Inference Configuration

The reported metrics were obtained using:

YOLOv8 object detector
SAHI sliced inference
Sliced size: 2000x2000
Overlap ratio: 0.0
Confidence treshold: 0.5

Limitations

Performance may vary under:

Strong occlusions
Extrem distance from target objects
Panoramic deformations Model was trained specifically on Google Street View imagery and may not generalize to other image domains without fine-tuning.

Citation

If you use this model in research, please cite the corresponding project or repository.

Author

Developed as part of a Master's Thesis (TFM) in Bioinformatics and Biostatistics

gasparfj/Opuntia-detector

Opuntia Detection from Google Street View