Unfolding Target Detection with State Space Model

• URL: http://arxiv.org/abs/2410.22774v1
• Date: Wed, 30 Oct 2024 07:43:18 GMT
• Title: Unfolding Target Detection with State Space Model
• Authors: Luca Jiang-Tao Yu, Chenshu Wu,
• Abstract summary: We introduce a novel method that combines signal processing and deep learning by unfolding the CFAR detector with a state space model architecture. By reserving the CFAR pipeline yet turning its sophisticated configurations into trainable parameters, our method achieves high detection performance without manual parameter tuning. The results highlight the remarkable performance of the proposed method, outperforming CFAR and its variants by 10X in detection rate and false alarm rate.
• Score: 8.493729039825332
• License:
• Abstract: Target detection is a fundamental task in radar sensing, serving as the precursor to any further processing for various applications. Numerous detection algorithms have been proposed. Classical methods based on signal processing, e.g., the most widely used CFAR, are challenging to tune and sensitive to environmental conditions. Deep learning-based methods can be more accurate and robust, yet usually lack interpretability and physical relevance. In this paper, we introduce a novel method that combines signal processing and deep learning by unfolding the CFAR detector with a state space model architecture. By reserving the CFAR pipeline yet turning its sophisticated configurations into trainable parameters, our method achieves high detection performance without manual parameter tuning, while preserving model interpretability. We implement a lightweight model of only 260K parameters and conduct real-world experiments for human target detection using FMCW radars. The results highlight the remarkable performance of the proposed method, outperforming CFAR and its variants by 10X in detection rate and false alarm rate. Our code is open-sourced here: https://github.com/aiot-lab/NeuroDet.

Related papers

• Efficient Meta-Learning Enabled Lightweight Multiscale Few-Shot Object Detection in Remote Sensing Images [15.12889076965307]
  YOLOv7 one-stage detector is subjected to a novel meta-learning training framework. This transformation allows the detector to adeptly address FSOD tasks while capitalizing on its inherent advantage of lightweight. To validate the effectiveness of our proposed detector, we conducted performance comparisons with current state-of-the-art detectors.
  arXiv  Detail & Related papers  (2024-04-29T04:56:52Z)
• SIRST-5K: Exploring Massive Negatives Synthesis with Self-supervised Learning for Robust Infrared Small Target Detection [53.19618419772467]
  Single-frame infrared small target (SIRST) detection aims to recognize small targets from clutter backgrounds. With the development of Transformer, the scale of SIRST models is constantly increasing. With a rich diversity of infrared small target data, our algorithm significantly improves the model performance and convergence speed.
  arXiv  Detail & Related papers  (2024-03-08T16:14:54Z)
• Global Context Aggregation Network for Lightweight Saliency Detection of Surface Defects [70.48554424894728]
  We develop a Global Context Aggregation Network (GCANet) for lightweight saliency detection of surface defects on the encoder-decoder structure. First, we introduce a novel transformer encoder on the top layer of the lightweight backbone, which captures global context information through a novel Depth-wise Self-Attention (DSA) module. The experimental results on three public defect datasets demonstrate that the proposed network achieves a better trade-off between accuracy and running efficiency compared with other 17 state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-09-22T06:19:11Z)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• Object-Size-Driven Design of Convolutional Neural Networks: Virtual Axle Detection based on Raw Data [0.0]
  This study addresses the challenge of replacing dedicated axle detectors with a novel approach to real-time detection of train axles. The proposed Virtual Axle Detector with Enhanced Receptive Field (VADER) has been validated on a single-track railway bridge. Using raw data as input outperforms the state-of-the-art spectrogram-based method in both speed and memory usage by 99%.
  arXiv  Detail & Related papers  (2023-09-04T12:53:54Z)
• Anomaly Detection with Ensemble of Encoder and Decoder [2.8199078343161266]
  Anomaly detection in power grids aims to detect and discriminate anomalies caused by cyber attacks against the power system. We propose a novel anomaly detection method by modeling the data distribution of normal samples via multiple encoders and decoders. Experiment results on network intrusion and power system datasets demonstrate the effectiveness of our proposed method.
  arXiv  Detail & Related papers  (2023-03-11T15:49:29Z)
• CFARnet: deep learning for target detection with constant false alarm rate [2.2940141855172036]
  We introduce a framework of CFAR constrained detectors. Practically, we develop a deep learning framework for fitting neural networks that approximate it. Experiments of target detection in different setting demonstrate that the proposed CFARnet allows a flexible tradeoff between CFAR and accuracy.
  arXiv  Detail & Related papers  (2022-08-04T05:54:36Z)
• Decision Forest Based EMG Signal Classification with Low Volume Dataset Augmented with Random Variance Gaussian Noise [51.76329821186873]
  We produce a model that can classify six different hand gestures with a limited number of samples that generalizes well to a wider audience. We appeal to a set of more elementary methods such as the use of random bounds on a signal, but desire to show the power these methods can carry in an online setting.
  arXiv  Detail & Related papers  (2022-06-29T23:22:18Z)
• Rethinking Drone-Based Search and Rescue with Aerial Person Detection [79.76669658740902]
  The visual inspection of aerial drone footage is an integral part of land search and rescue (SAR) operations today. We propose a novel deep learning algorithm to automate this aerial person detection (APD) task. We present the novel Aerial Inspection RetinaNet (AIR) algorithm as the combination of these contributions.
  arXiv  Detail & Related papers  (2021-11-17T21:48:31Z)
• Plug-and-Play Few-shot Object Detection with Meta Strategy and Explicit Localization Inference [78.41932738265345]
  This paper proposes a plug detector that can accurately detect the objects of novel categories without fine-tuning process. We introduce two explicit inferences into the localization process to reduce its dependence on annotated data. It shows a significant lead in both efficiency, precision, and recall under varied evaluation protocols.
  arXiv  Detail & Related papers  (2021-10-26T03:09:57Z)
• A Reinforcement Learning based approach for Multi-target Detection in Massive MIMO radar [12.982044791524494]
  This paper considers the problem of multi-target detection for massive multiple input multiple output (MMIMO) cognitive radar (CR) We propose a reinforcement learning (RL) based algorithm for cognitive multi-target detection in the presence of unknown disturbance statistics. Numerical simulations are performed to assess the performance of the proposed RL-based algorithm in both stationary and dynamic environments.
  arXiv  Detail & Related papers  (2020-05-10T16:29:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.