Enhancing Object Detection for Autonomous Driving by Optimizing Anchor Generation and Addressing Class Imbalance

by   Manuel Carranza-García, et al.

Object detection has been one of the most active topics in computer vision for the past years. Recent works have mainly focused on pushing the state-of-the-art in the general-purpose COCO benchmark. However, the use of such detection frameworks in specific applications such as autonomous driving is yet an area to be addressed. This study presents an enhanced 2D object detector based on Faster R-CNN that is better suited for the context of autonomous vehicles. Two main aspects are improved: the anchor generation procedure and the performance drop in minority classes. The default uniform anchor configuration is not suitable in this scenario due to the perspective projection of the vehicle cameras. Therefore, we propose a perspective-aware methodology that divides the image into key regions via clustering and uses evolutionary algorithms to optimize the base anchors for each of them. Furthermore, we add a module that enhances the precision of the second-stage header network by including the spatial information of the candidate regions proposed in the first stage. We also explore different re-weighting strategies to address the foreground-foreground class imbalance, showing that the use of a reduced version of focal loss can significantly improve the detection of difficult and underrepresented objects in two-stage detectors. Finally, we design an ensemble model to combine the strengths of the different learning strategies. Our proposal is evaluated with the Waymo Open Dataset, which is the most extensive and diverse up to date. The results demonstrate an average accuracy improvement of 6.13 9.69 do not increase computational cost and can easily be extended to optimize other anchor-based detection frameworks.


page 4

page 5

page 9

page 11

page 20


2nd Place Solution for Waymo Open Dataset Challenge – 2D Object Detection

A practical autonomous driving system urges the need to reliably and acc...

Pillar-based Object Detection for Autonomous Driving

We present a simple and flexible object detection framework optimized fo...

Balance-Oriented Focal Loss with Linear Scheduling for Anchor Free Object Detection

Most existing object detectors suffer from class imbalance problems that...

AFDet: Anchor Free One Stage 3D Object Detection

High-efficiency point cloud 3D object detection operated on embedded sys...

Foreground-Background Imbalance Problem in Deep Object Detectors: A Review

Recent years have witnessed the remarkable developments made by deep lea...

Research on road object detection algorithm based on improved YOLOX

Road object detection is an important branch of automatic driving techno...

Class-specific Anchoring Proposal for 3D Object Recognition in LIDAR and RGB Images

Detecting objects in a two-dimensional setting is often insufficient in ...

Please sign up or login with your details

Forgot password? Click here to reset