DALDet: Depth-Aware Learning Based Object Detection for Autonomous Driving
Authors: Ke Hu, Tongbo Cao, Yuan Li, Song Chen, Yi Kang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the superiority and efficiency of DALDet. In particular, our DALDet ranks 1st on both KITTI Car and Cyclist 2D detection test leaderboards among all 2D detectors with high efficiency as well as yielding competitive performance among many leading 3D detectors. |
| Researcher Affiliation | Academia | 1University of Science and Technology of China, Hefei, China 2Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China 3Anhui University, Hefei, China |
| Pseudocode | No | The paper describes the model architecture and components but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code will be available at https://github.com/hukefy/DALDet. |
| Open Datasets | Yes | KITTI Dataset The KITTI dataset (Geiger, Lenz, and Urtasun 2012) is a popular benchmark for autonomous driving |
| Dataset Splits | Yes | We divided the training data into a training set with 3712 samples and a validation set with 3769 samples following (Chen et al. 2015). |
| Hardware Specification | Yes | The model training and testing were conducted using Py Torch framework on NVIDIA Ge Force RTX 3090 GPU card. |
| Software Dependencies | No | The paper mentions using the 'Py Torch framework' but does not specify a version number or other software dependencies with version numbers. |
| Experiment Setup | Yes | The initial learning rate, batch size, and total number of epochs were set to 0.01, 32, and 300, respectively. During testing, we selected an Io U threshold of 0.3 for post-processing, and a maximum of 100 predictions were saved per image. |