yscv-detect

Object detection pipeline: YOLOv8/v11 decoding, NMS, heatmap detection, ROI pooling, and anchor generation.

use yscv_detect::*;

let detections = detect_people_from_rgb8(width, height, &rgb_data, 0.5, 3, 0.45, 100)?;
for det in &detections {
    println!("{}: {:.0}% at ({}, {}, {}, {})",
        det.label, det.score * 100.0, det.x, det.y, det.w, det.h);
}

Features

• YOLO decoding: YOLOv8 and YOLOv11 output tensor parsing with letterbox preprocessing
• NMS: standard, soft-NMS, batched (class-aware), with early exit optimization
• Heatmap detection: keypoint-based detection with local maxima suppression
• ROI ops: ROI pooling and bilinear ROI align
• Anchors: multi-scale anchor generation for SSD/Faster R-CNN
• Scratch buffers: zero-alloc detection with reusable scratch objects

Optional Features

[features]
onnx = []  # ONNX model inference via yscv-onnx

Tests

60 tests covering decoding, NMS edge cases, ROI alignment.