FIELD

The disclosure relates generally to small objection detection using generative adversarial network models. More specifically, the disclosure relates to small objection detection in roadway scenes using generative adversarial network models.

BACKGROUND

Roadway scene understanding is important in autonomous driving applications. Object detection plays an important role in roadway scene understanding. However, detecting small objects in image data (for example, traffic lights) is challenging because small objects are generally located far away from the detector and have very low contrast compared to their surroundings.

SUMMARY

Deep detector models are used for detecting objects in image data. However, small roadway object detection remains a challenge for deep detector models. Thus, the disclosure provides, among other things, systems, methods, and non-transitory computer-readable media for detecting small objects in a roadway scene.

The disclosure provides a system for detecting small objects in a roadway scene. In some implementations, the system includes a camera and an electronic controller. The camera is coupled to a vehicle and configured to capture a roadway scene image. The electronic controller is coupled to the camera. The electronic controller is configured to receive the roadway scene image from the camera. The electronic controller is also configured to generate a Generative Adversarial Network (GAN) model using the roadway scene image. The electronic controller is further configured to determine a distribution indicting how likely each location in the roadway scene image can contain a roadway object using the GAN model. The electronic controller is also configured to determine a plurality of locations in the roadway scene image by sampling the distribution. The electronic controller is further configured to detect the roadway object at one of the plurality of locations in the roadway scene image.

The disclosure also provides a method for detecting small objects in a roadway scene. The method includes receiving, with an electronic processor, a roadway scene image from a camera coupled to a vehicle. The method also includes generating, with the electronic processor, a Generative Adversarial Network (GAN) model using the roadway scene image. The method further includes determining, with the electronic processor, a distribution indicting how likely each location in the roadway scene image can contain a roadway object using the GAN model. The method also includes determining, with the electronic processor, a plurality of locations in the roadway scene image by sampling the distribution. The method further includes detecting, with the electronic processor, the roadway object at one of the plurality of locations in the roadway scene image.

The disclosure also provides a non-transitory computer-readable medium storing computer-readable instructions that, when executed by an electronic processor of a computer, cause the computer to perform operations. The operations include receiving a roadway scene image from a camera coupled to a vehicle. The operations also include generating a Generative Adversarial Network (GAN) model using the roadway scene image. The operations further include determining a distribution indicting how likely each location in the roadway scene image can contain a roadway object using the GAN model. The operations also include determining a plurality of locations in the roadway scene image by sampling the distribution. The operations further include detecting the roadway object at one of the plurality of locations in the roadway scene image.

