# grid_mamba_stream **Repository Path**: designer7458/grid_mamba_stream ## Basic Information - **Project Name**: grid_mamba_stream - **Description**: grid_mamba_stream完整版，开发分支在new_grid_mamba_stream - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-09-14 - **Last Updated**: 2026-07-21 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # 🚁 Event-based Tiny Object Detection: A Benchmark Dataset and Baseline

The official implementation of "Event-based Tiny Object Detection: A Benchmark Dataset and Baseline" (ICCV 2025) [Paper](https://arxiv.org/abs/2506.23575) ## 🌟 Abstract Small object detection (SOD) in anti-UAV task is a challenging problem due to the small size of UAVs and complex backgrounds. Traditional frame-based cameras struggle to detect small objects in complex environments due to their low frame rates, limited dynamic range, and data redundancy. Event cameras, with microsecond temporal resolution and high dynamic range, provide a more effective solution for SOD. However, existing event-based object detection datasets are limited in scale, feature large targets size, and lack diverse backgrounds, making them unsuitable for SOD benchmarks. In this paper, we introduce a Event-based Small object detection (EVSOD) dataset (namely **EV-UAV**), the first large-scale, highly diverse benchmark for anti-UAV tasks. It includes 147 sequences with over **2.3 million event-level annotations,** featuring **extremely small targets** (averaging 6.8 × 5.4 pixels) and **diverse scenarios** such as urban clutter and extreme lighting conditions. Furthermore, based on the observation that small moving targets form continuous curves in spatiotemporal event point clouds, we propose Event based Sparse Segmentation Network (EV-SpSegNet), a novel baseline for event segmentation in point cloud space, along with a Spatiotemporal Correlation (STC) loss that leverages motion continuity to guide the network in retaining target events. Extensive experiments on the EV-UAV dataset demonstrate the superiority of our method and provide a benchmark for future research in EVSOD. --- ## 📊EV-UAV dataset - ### Comparison between event camera and RGB camera

The RGB camera can only capture the objects under normal light, while the event camera can capture objects under various extreme lighting conditions. And the event camera can capture the continuous motion trajectory of the small object (shown as the red curve). - ### Comparison between EV-UAV and other event-based object detection datasets
Dataset #AGV.UAV scale Label Type UAV Sequence Ratio UAV centric Lighting conditions Object Year
BL NL LL MS MT
VisEvent 84×66 pixels BBox 15.97 × × √ √ √ × 2023
EventVOT 129×100 pixels BBox 8.41 × × √ √ √ × 2024
EvDET200K 68×45 pixels BBox 3.57 × × √ √ √ √ 2024
F-UAV-D - BBox 100 √ × √ × × × 2024
NeRDD 55×31 pixels BBox 100 √ × √ × × √ 2024
EV-UAV 6.8×5.4 pixels Seg 100 √ √ √ √ √ √ 2025
Currently, event-based object detection datasets primarily focus on autonomous driving and general object detection. There is limited attention given to datasets that are exclusively designed for UAV detection. We provide a comprehensive summary of existing datasets, highlighting the scarcity of event-based datasets for UAV object detection. - ### Benchmark Features and Statistics EV-UAV contains 147 event sequences with **event-level annotations**, covering **challenging scenarios** like high-brightness and low-light conditions, with **targets** **averaging 1/50 the size in existing datasets**. --- ## 📂 Structure of EV-UAV The file structure of the dataset is as follows: ``` EV-UAV/ ├── test/ │ ├── test_000.npz │ ├── test_001.npz │ ├──..... ├── train ├── val ``` --- ## 📝 Data Format Event data is stored in `.npz` format, it contains three files (i.e., 'evs_norm', 'ev_loc' and 'ev'). **'ev'** is the raw event data. - **x, y:** Pixel coordinates of the event. - **timestamp:** Time of event occurrence (microseconds). - **polarity:** Polarity of brightness change (+1 or -1). - **label:** Indicates if it's the target (0 or 1). - **id:** Identity of the target . Example: ``` x y timestamp polarity label id 100 200 1 1 0 0 128 258 4000 -1 1 5 ``` **'evs_norm'** is the normalized event data. Example: ``` x y timestamp polarity label id 0.289 0.769 0 1 0 0 0.369 0.992 0.5 -1 1 5 ``` **'ev_loc'** is the coordinate of the event in point cloud space. Example: ``` x y timestamp 100 200 1 128 258 4000 ``` ## ⬇️ Dataset The EV-UAV dataset can be download from [Baidu Netdisk](https://pan.baidu.com/s/15pAlu3KP1uXych-c3SC5qA?pwd=sbr2). Extracted code: sbr2 --- # :triangular_flag_on_post:Baseline Leveraging the spatiotemporal correlation characteristics of moving targets in event data, we propose EV-SpSegNet, a direct segmentation network for sparse event point clouds, and design a spatiotemporal correlation loss function that optimizes target event extraction by evaluating local spatiotemporal consistency. ### Event based Sparse Segmentation Network Event based Sparse Segmentation Network (EV-SpSegNet) employs a U-shaped encoder-decoder architecture, integrating three key components: the GDSCA module (Grouped Dilated Sparse Convolution) for multi-scale temporal feature extraction, the Sp-SE module for feature fusion, and the Patch Attention block for voxel downsampling and global context modeling. - ### STCLOSS We introduce a spatiotemporal correlation loss that encourages the network to retain more events with high spatiotemporal correlation while discarding more isolated noise.

# 🚀Installation for docker Please refer to [docker_for_evuav](https://github.com/ChenYichen9527/EV-UAV/blob/main/docker_for_evuav.md) # 🚀Installation 1) Create a new conda environment ``` conda create -n evuav python=3.8 conda activate evuav ``` 2) Install dependencies ``` conda install pytorch==1.9.1 torchvision==0.10.1 torchaudio==0.9.1 cudatoolkit=11.3 -c pytorch -c conda-forge ``` 3) Install [spconv](https://github.com/traveller59/spconv) 4) Compile the external C++ and CUDA ops. ``` cd ev-spsegnet/lib/hais_ops export CPLUS_INCLUDE_PATH={conda_env_path}/hais/include:$CPLUS_INCLUDE_PATH python setup.py build_ext develop ``` ## 🎯Running code **1) Configuration file**: change the dataset root and the model save root by yourself ```python cd configs/evisseg_evuav.yaml ``` **2) Training** ```python train.py ``` **3) Testing** ```python test.py ``` **4) Pre_trained weights** The pre_trained weights can be download [here](https://pan.baidu.com/s/1e6a_Ool5WZ3cBMPvoJvWbg?pwd=ztp4). Extracted code:ztp4 --- ## Citation If you use this work in your research, please cite it: ```bibtex @misc{chen2025eventbasedtinyobjectdetection, title={Event-based Tiny Object Detection: A Benchmark Dataset and Baseline}, author={Nuo Chen and Chao Xiao and Yimian Dai and Shiman He and Miao Li and Wei An}, year={2025}, eprint={2506.23575}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2506.23575}, } ``` --- ## Acknowledgement The code is based on [HAIS](https://github.com/hustvl/HAIS) and [spconv](https://github.com/traveller59/spconv).