pedestrian-and-vehicle-detector-adas-0001

Use Case and High-Level Description

Pedestrian and vehicle detection network based on MobileNet v1.0 + SSD.

Example

pedestrian-and-vehicle-detector-adas-0001.png

Specification

Metric Value
AP for pedestrians 88%
AP for vehicles 90%
Target pedestrian size 60x120 pixels
Target vehicle size 40x30 pixels
GFLOPS 3.974
MParams 1.650
Source framework Caffe*

Average Precision (AP) metric is described in: Mark Everingham et al. “The PASCAL Visual Object Classes (VOC) Challenge”.

Tested on challenging internal datasets with 1001 pedestrian and 12585 vehicles to detect.

Performance

Link to performance table

Inputs

  1. name: "input" , shape: [1x3x384x672] - An input image in the format [BxCxHxW], where:
    • B - batch size
    • C - number of channels
    • H - image height
    • W - image width. Expected color order is BGR.

Outputs

  1. The net outputs 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]
    • 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

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