SpirDet: Towards Efficient, Accurate and Lightweight Infrared Small Target Detector

• URL: http://arxiv.org/abs/2402.05410v1
• Date: Thu, 8 Feb 2024 05:06:14 GMT
• Title: SpirDet: Towards Efficient, Accurate and Lightweight Infrared Small Target Detector
• Authors: Qianchen Mao, Qiang Li, Bingshu Wang, Yongjun Zhang, Tao Dai, C.L. Philip Chen
• Abstract summary: We propose SpirDet, a novel approach for efficient detection of infrared small targets. We employ a new dual-branch sparse decoder to restore the feature map. Extensive experiments show that the proposed SpirDet significantly outperforms state-of-the-art models.
• Score: 60.42293239557962
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, the detection of infrared small targets using deep learning methods has garnered substantial attention due to notable advancements. To improve the detection capability of small targets, these methods commonly maintain a pathway that preserves high-resolution features of sparse and tiny targets. However, it can result in redundant and expensive computations. To tackle this challenge, we propose SpirDet, a novel approach for efficient detection of infrared small targets. Specifically, to cope with the computational redundancy issue, we employ a new dual-branch sparse decoder to restore the feature map. Firstly, the fast branch directly predicts a sparse map indicating potential small target locations (occupying only 0.5\% area of the map). Secondly, the slow branch conducts fine-grained adjustments at the positions indicated by the sparse map. Additionally, we design an lightweight DO-RepEncoder based on reparameterization with the Downsampling Orthogonality, which can effectively reduce memory consumption and inference latency. Extensive experiments show that the proposed SpirDet significantly outperforms state-of-the-art models while achieving faster inference speed and fewer parameters. For example, on the IRSTD-1K dataset, SpirDet improves $MIoU$ by 4.7 and has a $7\times$ $FPS$ acceleration compared to the previous state-of-the-art model. The code will be open to the public.

Related papers

• ESOD: Efficient Small Object Detection on High-Resolution Images [36.80623357577051]
  Small objects are usually sparsely distributed and locally clustered. Massive feature extraction computations are wasted on the non-target background area of images. We propose to reuse the detector's backbone to conduct feature-level object-seeking and patch-slicing.
  arXiv  Detail & Related papers  (2024-07-23T12:21:23Z)
• Infrared Small Target Detection with Scale and Location Sensitivity [19.89762494490961]
  In this paper, we focus on boosting detection performance with a more effective loss but a simpler model structure. Specifically, we first propose a novel Scale and Location Sensitive (SLS) loss to handle the limitations of existing losses. By applying SLS loss to each scale of the predictions, our MSHNet outperforms existing state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2024-03-28T12:28:58Z)
• 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)
• One-Stage Cascade Refinement Networks for Infrared Small Target Detection [21.28595135499812]
  Single-frame InfraRed Small Target (SIRST) detection has been a challenging task due to a lack of inherent characteristics. We present a new research benchmark for infrared small target detection consisting of the SIRST-V2 dataset of real-world, high-resolution single-frame targets.
  arXiv  Detail & Related papers  (2022-12-16T13:37:23Z)
• Lightweight Salient Object Detection in Optical Remote Sensing Images via Feature Correlation [93.80710126516405]
  We propose a novel lightweight ORSI-SOD solution, named CorrNet, to address these issues. By reducing the parameters and computations of each component, CorrNet ends up having only 4.09M parameters and running with 21.09G FLOPs. Experimental results on two public datasets demonstrate that our lightweight CorrNet achieves competitive or even better performance compared with 26 state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-01-20T08:28:01Z)
• Infrared Small-Dim Target Detection with Transformer under Complex Backgrounds [155.388487263872]
  We propose a new infrared small-dim target detection method with the transformer. We adopt the self-attention mechanism of the transformer to learn the interaction information of image features in a larger range. We also design a feature enhancement module to learn more features of small-dim targets.
  arXiv  Detail & Related papers  (2021-09-29T12:23:41Z)
• QueryDet: Cascaded Sparse Query for Accelerating High-Resolution Small Object Detection [17.775203579232144]
  We propose a novel query mechanism to accelerate the inference speed of feature-pyramid based object detectors. The pipeline first predicts the coarse locations of small objects on low-resolution features and then computes the accurate detection results using high-resolution features. On the popular COCO dataset, the proposed method improves the detection mAP by 1.0 and mAP-small by 2.0, and the high-resolution inference speed is improved to 3.0x on average.
  arXiv  Detail & Related papers  (2021-03-16T15:30:20Z)
• Dense Label Encoding for Boundary Discontinuity Free Rotation Detection [69.75559390700887]
  This paper explores a relatively less-studied methodology based on classification. We propose new techniques to push its frontier in two aspects. Experiments and visual analysis on large-scale public datasets for aerial images show the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-11-19T05:42:02Z)
• Anchor-free Small-scale Multispectral Pedestrian Detection [88.7497134369344]
  We propose a method for effective and efficient multispectral fusion of the two modalities in an adapted single-stage anchor-free base architecture. We aim at learning pedestrian representations based on object center and scale rather than direct bounding box predictions. Results show our method's effectiveness in detecting small-scaled pedestrians.
  arXiv  Detail & Related papers  (2020-08-19T13:13:01Z)

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.