Models

The Live Sense models provide an interface for interacting with the underlying machine learning (ML) models used to detect objects.

The core function of every model is to take in an RGB image and return a list of detections found within the image.

Each Live Sense model has a set of object classes that it is able to detect. It is up to the developer to determine which objects, and therefore models, are relevant to their use case.

Model types and detection classes

Models available in beta mode

The models below are available for beta testing:

Confidence configuration

Live Sense SDK for Linux allows you to configure the confidence values of the models.

Update the confidence of a model

You may set the minimum confidence threshold for all classes available in a specified model using the model classes' respective options struct. The options are set during model initialization and may be updated via a setter method.

// Road Basics
ls::RoadBasicsModelOptions roadBasicsOptions;
roadBasicsOptions.roadBasicsConfidenceThresh = 0.45;
roadBasicsOptions.trafficLightConfidenceThresh = 0.75;
// Road Hazards
std::vector<ls::RoadHazardsModelOptions> roadHazardsModelOptions = {
    {ls::RoadHazardsModelType::RH_CONE_CONSTRUCTION, 0.6},
    {ls::RoadHazardsModelType::RH_POTHOLES, 0.6},
    {ls::RoadHazardsModelType::RH_SPEED_BUMP_OBJECT, 0.55},
    {ls::RoadHazardsModelType::RH_SPEED_BUMP_SIGNAGE, 0.55},
    {ls::RoadHazardsModelType::RH_BRIDGE_TUNNEL, 0.75},
    {ls::RoadHazardsModelType::RH_HEIGHT_RESTRICTION_SIGNS, 0.6}};
// Road Signs
ls::RoadSignsModelOptions roadSignsOptions;
roadSignsOptions.roadSignsConfidenceThresh = 0.55;
// Brake Light
ls::BrakeLightModelOptions brakeLightOptions;
brakeLightOptions.brakeLightConfidenceThresh = 0.8;

For a model, the confidence threshold here can be used from the 'Recommended Confidence' described in the Model Types and Detection Classes table.

Limitations

Using a lower confidence threshold will result in more detections, but an overall lower accuracy. This can sometimes cause the labels to show false positives. For this reason, applications should keep their confidence threshold at or above 0.6 to maintain reasonably accurate output.

Threading

The Live Sense models are not thread-safe and should be both initialized and executed on the same thread. Utilizing multiple threads for the same model instance may result in unexpected behavior.

Models may be executed in parallel, but each model instance can only handle one image at a time. Executing a model before the previous call has completed will result in an exception.

Model execution options

Live Sense SDK for Linux supports multiple instances for executing the Live Sense models. Exact support depends upon the device and model being used.

Object recognition

A Live Sense Recognition describes the following properties of a detected object:

• class - What object was detected. For more information, see Model Types and Detection Classes.
• location - Where the object was found in the image frame.
• confidence score - A number between 0 and 1 that indicates confidence that the object was correctly detected.

Model types

The following libraries are supported:

• TensorFlow Lite (TFLite)
• TensorRT
• Coral Edge TPU

Engine runtime support

For the TensorFlow Lite variant to load a model on GPU, you must set the appropriate model option to true.

ls::ModelOptions mOptions;
// Enable GPU delegate in TensorFlow Lite SDK variant
mOptions.setUseGpuDelegate(true);

Model runtime support

By default, the SDK will run fp32 variant. You can enable fp16 or INT8 by setting the respective variable to TRUE in the model options. Ensure that only one variant is set to TRUE at a time.

ls::ModelOptions mOptions;
mOptions.setRunInFp16(true);
//OR
mOptions.setRunInInt8(true);

Distance and position estimation

All of the object detection models listed above can provide an estimated distance and position relative to the camera's point of view for each detected object.

The output values for the distance and relativePosition properties of a detected object are as follows:

• Height: Distance of the detection from the ground.
• Lateral: Distance of the detection relative to the center of the camera's view. This value may be positive or negative with a negative value signifying that the detection is towards the left side of the camera's view and vice versa.
• Depth: Distance that the device needs to travel forward in a straight line so that the actual distance is equal to the lateral distance.
• Distance: Actual distance of the detection from the camera's point of view.

All distance values are provided in meters and are used in the following features of Live Sense SDK

• Time-to-collision alerts
• Relative velocity estimation

Road signs

Group	Label	Sample Image
SpeedLimit	DS_SpeedLimitXX_White_Circle_00
	DS_SpeedLimitXX_White_Circle_01
	DS_SpeedLimitXX_White_Rectangle_01
AdvisorySpeedLimit	DS_AdvisorySpeedLimitXX_Yellow_Rectangle_00
	DS_AdvisorySpeedLimitXX_Yellow_Rectangle_01
	DS_AdvisorySpeedLimitXX_Yellow_Rectangle_03
Stop	DS_Stop_Red_Octogon_00
DoNotEnter	DS_DoNotEnter_Red_Circle_00
	DS_DoNotEnter_White_Rectangle_00
	DS_DoNotEnter_White_Rectangle_01
NoAccess	DS_NoAccess_White_Circle_00
StartConstruction	DS_StartConstruction_White_Triangle_00
Yield	DS_Yield_White_Triangle_00
PedstrianCrossing	DS_PedestrianCrossing_Blue_Rectangle_00
	DS_PedestrianCrossing_White_Triangle_00
	DS_PedestrianCrossing_Yellow_Circle_00
	DS_PedestrianCrossing_Yellow_Diamond_01
	DS_PedestrianCrossing_Yellow_Triangle_00
SpeedBumps	DS_SpeedBumps_Blue_Rectangle_00
	DS_SpeedBumps_White_Triangle_00
	DS_SpeedBumps_Yellow_Diamond_01
StopHereOnRed	DS_StopHereOnRed_White_Rectangle_00
NoLeftTurn	DS_NoLeftTurn_White_Circle_00
	DS_NoLeftTurn_White_Rectangle_02
NoRightOrLeftTurn	DS_NoRightOrLeftTurn_White_Rectangle_00
	DS_NoRightOrLeftTurn_White_Circle_00
NoUTurn	DS_NoUTurn_White_Circle_00
NoUTurnOrLeftTurn	DS_NoUTurnOrLeftTurn_White_Rectangle_00
ParkingRestriction	DS_ParkingRestriction_Blue_Circle_00
	DS_ParkingRestriction_White_Circle_01
	DS_ParkingRestriction_White_Rectangle_03