vehicle-detection-adas-0002

Use Case and High-Level Description

This is a vehicle detection network based on an SSD framework with tuned MobileNet v1 as a feature extractor.

Example

Specification

Metric	Value
Average Precision (AP)	90.6%
Target vehicle size	40 x 30 pixels on Full HD image
Max objects to detect	200
GFlops	2.798
MParams	1.079
Source framework	Caffe*

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

Tested on a challenging internal dataset with 3000 images and 12585 vehicles to detect.

Performance

Inputs

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

The net outputs a blob with the 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

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