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person-vehicle-bike-detection-crossroad-0078

Use Case and High-Level Description

Person/Vehicle/Bike detector is based on SSD detection architecture, RMNet backbone, and learnable image downscale block (like person-vehicle-bike-detection-crossroad-0066, but with extra pooling). The model is intended for security surveillance applications and works in a variety of scenes and weather/lighting conditions.

Example

Specification

Metric Value
Mean Average Precision (mAP) 64.48%
AP people 76.22%
AP vehicles 74.66%
AP bikes 42.56%
Max objects to detect 200
GFlops 3.964
MParams 1.178
Source framework Caffe*

Average Precision (AP) is defined as an area under the precision/recall curve.

Validation dataset consists of 34,757 images from various scenes and includes:

Type of object Number of bounding boxes
Vehicle 229,503
Pedestrian 240,009
Bike 62,643

Similarly, training dataset has 160,297 images with:

Type of object Number of bounding boxes
Vehicle 501,548
Pedestrian 706,786
Bike 55,692

Performance

Inputs

  1. name: "input" , shape: [1x3x1024x1024] - An input image in the format [BxCxHxW], where:

    • B - batch size
    • C - number of channels
    • H - image height
    • W - image width

    The expected color order is BGR.

Outputs

  1. The net outputs a blob with shape: [1, 1, N, 7], where N is the number of detected bounding boxes. For each detection, the description has the format: [image_id, label, conf, x_min, y_min, x_max, y_max], where:

    • image_id - ID of the image in the batch
    • label - predicted class ID
    • conf - confidence for the predicted class
    • (x_min, y_min) - coordinates of the top left bounding box corner
    • (x_max, y_max) - coordinates of the bottom right bounding box corner

Legal Information

[*] Other names and brands may be claimed as the property of others.