Replication Study and Benchmarking of Real-Time Object Detection Models

This work examines the reproducibility and benchmarking of state-of-the-art real-time object detection models. As object detection models are often used in real-world contexts, such as robotics, where inference time is paramount, simply measuring models' accuracy is not enough to compare them. We thus compare a large variety of object detection models' accuracy and inference speed on multiple graphics cards. In addition to this large benchmarking attempt, we also reproduce the following models from scratch using PyTorch on the MS COCO 2017 dataset: DETR, RTMDet, ViTDet and YOLOv7. More importantly, we propose a unified training and evaluation pipeline, based on MMDetection's features, to better compare models. Our implementation of DETR and ViTDet could not achieve accuracy or speed performances comparable to what is declared in the original papers. On the other hand, reproduced RTMDet and YOLOv7 could match such performances. Studied papers are also found to be generally lacking for reproducibility purposes. As for MMDetection pretrained models, speed performances are severely reduced with limited computing resources (larger, more accurate models even more so). Moreover, results exhibit a strong trade-off between accuracy and speed, prevailed by anchor-free models - notably RTMDet or YOLOx models. The code used is this paper and all the experiments is available in the repository at https://github.com/Don767/segdet_mlcr2024.

Proprioception Is All You Need: Terrain Classification for Boreal Forests

Recent works in field robotics highlighted the importance of resiliency against different types of terrains. Boreal forests, in particular, are home to many mobility-impeding terrains that should be considered for off-road autonomous navigation. Also, being one of the largest land biomes on Earth, boreal forests are an area where autonomous vehicles are expected to become increasingly common. In this paper, we address this issue by introducing BorealTC, a publicly available dataset for proprioceptive-based terrain classification (TC). Recorded with a Husky A200, our dataset contains 116 min of Inertial Measurement Unit (IMU), motor current, and wheel odometry data, focusing on typical boreal forest terrains, notably snow, ice, and silty loam. Combining our dataset with another dataset from the state-of-the-art, we evaluate both a Convolutional Neural Network (CNN) and the novel state space model (SSM)-based Mamba architecture on a TC task. Interestingly, we show that while CNN outperforms Mamba on each separate dataset, Mamba achieves greater accuracy when trained on a combination of both. In addition, we demonstrate that Mamba's learning capacity is greater than a CNN for increasing amounts of data. We show that the combination of two TC datasets yields a latent space that can be interpreted with the properties of the terrains. We also discuss the implications of merging datasets on classification. Our source code and dataset are publicly available online: https://github.com/norlab-ulaval/BorealTC.

MaskBEV: Joint Object Detection and Footprint Completion for Bird's-eye View 3D Point Clouds

Recent works in object detection in LiDAR point clouds mostly focus on predicting bounding boxes around objects. This prediction is commonly achieved using anchor-based or anchor-free detectors that predict bounding boxes, requiring significant explicit prior knowledge about the objects to work properly. To remedy these limitations, we propose MaskBEV, a bird's-eye view (BEV) mask-based object detector neural architecture. MaskBEV predicts a set of BEV instance masks that represent the footprints of detected objects. Moreover, our approach allows object detection and footprint completion in a single pass. MaskBEV also reformulates the detection problem purely in terms of classification, doing away with regression usually done to predict bounding boxes. We evaluate the performance of MaskBEV on both SemanticKITTI and KITTI datasets while analyzing the architecture advantages and limitations.