| Abstract | Formula Student Driverless is a competition that provides an opportunity to design and test a wide set of technologies that are key for achieving autonomy in vehicle navigation: object detection and segmentation, obstacle avoidance, trajectory planning and following, etc.
In this context, the objective of this work is to employ and analyse state-of-the art Convolutional Neural Networks (CNN) architectures known in the literature for robust detection of cones whose function is to delimit tracks for an autonomous race car in the Formula Student Driverless Competition.
Here, fast and reliable cone detection is important for achieving a robust and efficient perception pipeline for a Formula race car.
To that end, we employ the known YOLO (You Only Look Once) architecture that makes use of CNNs and several optimizations to achieve fast and accurate enough object detection to be suitable for the current application.
To train the CNN/YOLO, different datasets are considered: one is provided by MIT and another is the FSOCO dataset (Formula Student Objects in Context), which is a more complete dataset that considers four different classes of cones in the track: blue cone, yellow cone, and small orange and large orange cones.
The main metrics for measuring the performance of the trained network in cone detection tasks are the Mean Average Precision (mAP) and network inference time.
Using these metrics and different versions of YOLO (the original and the tiny version), we analyze:
(1) the influence of the image resolution for the input layer of the CNN;
(2) the influence of different image conditions by varying brightness, exposure, blur, noise and other perturbations;
(3) the impact of manual augmentations in the training dataset and its performance under different image conditions.
In general, YOLO has shown to be a strong candidate for real-time cone detection for race cars.
Results shows that mAP performance increases as the image resolution for the input layer in the YOLO net gets higher, at a cost of increasing the CNN's inference time though.
Besides, manual augmentations of the training set were found to be effective for recovering the lost mAP when perturbations (of brightness, blur and noise) were added to the images.
As future work, the current cone detection solution can be extended to be part of the perception pipeline of the race car from Ampera Racing, which is an electric Formula SAE (Society of Automotive Engineers) team from Federal University of Santa Catarina. |
