2.3 KiB
2.3 KiB
ai_shoe_det - 鞋子检测节点
专门针对鞋子检测优化的节点,支持滑动窗口提高小目标检测率。
特性
- 滑动窗口支持:可配置多窗口覆盖全图,提高小鞋子检测精度
- 单类优化:专门针对 shoe 单类检测优化
- 自动 NMS:多窗口结果自动合并去重
- 轻量快速:基于 RK3588 NPU 加速
配置参数
{
"id": "shoe_det",
"type": "ai_shoe_det",
"model_path": "./models/shoe_detector.rknn",
"model_w": 640,
"model_h": 640,
"conf": 0.25,
"nms": 0.45,
"windows": [
{"x": 0, "y": 0, "w": 960, "h": 1080},
{"x": 960, "y": 0, "w": 960, "h": 1080}
]
}
参数说明
| 参数 | 类型 | 默认值 | 说明 |
|---|---|---|---|
model_path |
string | - | RKNN 模型路径 |
model_w |
int | 640 | 模型输入宽度 |
model_h |
int | 640 | 模型输入高度 |
conf |
float | 0.25 | 置信度阈值 |
nms |
float | 0.45 | NMS IoU 阈值 |
windows |
array | - | 滑动窗口配置,不配置则使用全图单窗口 |
窗口配置
- 单窗口(全图):不配置
windows或配置[{"x":0,"y":0,"w":0,"h":0}] - 双窗口(推荐):左右各 960x1080
"windows": [ {"x": 0, "y": 0, "w": 960, "h": 1080}, {"x": 960, "y": 0, "w": 960, "h": 1080} ]
Pipeline 示例
{
"nodes": [
{"id": "in", "type": "input_rtsp", "url": "rtsp://..."},
{"id": "pre", "type": "preprocess", "dst_w": 1920, "dst_h": 1080, "dst_format": "rgb"},
{
"id": "shoe_det",
"type": "ai_shoe_det",
"model_path": "./models/shoe_detector_openimages_ppe_v1.rknn",
"model_w": 640,
"model_h": 640,
"conf": 0.25,
"windows": [
{"x": 0, "y": 0, "w": 960, "h": 1080},
{"x": 960, "y": 0, "w": 960, "h": 1080}
]
},
{"id": "osd", "type": "osd"},
{"id": "pub", "type": "publish"}
],
"edges": [
["in", "pre"],
["pre", "shoe_det"],
["shoe_det", "osd"],
["osd", "pub"]
]
}
编译
cd build
cmake ..
make ai_shoe_det -j4
注意事项
- 模型必须是单类(shoe)YOLOv8 格式
- 多窗口会增加 NPU 负载(2窗口 = 2倍推理时间)
- 窗口之间有重叠时,NMS 会自动去重