MS-AFF: a novel semantic segmentation approach for buried object based on multi-scale attentional feature fusion

نویسندگان

چکیده

Infrared technology is widely used in buried object detection since it can capture the heat radiated outward from target objects. Compared to visible images, infrared images present poor resolution, low contrast, and fuzzy visual effect, making challenging detect objects, specifically complex backgrounds. In recent years, deep learning-based methods have made significant improvements tasks. However, of objects are difficult obtain. Less training samples, worse performance deep-learning based detection. We raise a multi-scale attentional feature fusion module for image semantic segmentation solve this problem. Precisely, we integrate series maps different levels by an atrous spatial pyramid structure. way, model obtain rich representation ability on images. Besides, global information attention employed let focus region reduce disturbance images’ background. addition, propose dataset thermal imaging system. Finally, use state-of-the-art train compare them with our proposed method. Extensive experiments demonstrate superiority

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

A novel feature extraction approach for microarray data based on multi-algorithm fusion

Feature extraction is one of the most important and effective method to reduce dimension in data mining, with emerging of high dimensional data such as microarray gene expression data. Feature extraction for gene selection, mainly serves two purposes. One is to identify certain disease-related genes. The other is to find a compact set of discriminative genes to build a pattern classifier with r...

متن کامل

Object Tracking Based on Camshift with Multi-feature Fusion

It is very hard for traditional Camshift to survive of drastic interferences and occlusions of similar objects. This paper puts forward an innovative tracking method using Camshift with multi-feature fusion. Firstly, SIFT features and edge features of the Camshift in RGB space are counted to reduce the probability of disruption by occlusion and clutter. Then, the texture features are collected ...

متن کامل

Multi-Evidence Filtering and Fusion for Multi-Label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning

Supervised object detection and semantic segmentation require object or even pixel level annotations. When there exist image level labels only, it is challenging for weakly supervised algorithms to achieve accurate predictions. The accuracy achieved by top weakly supervised algorithms is still significantly lower than their fully supervised counterparts. In this paper, we propose a novel weakly...

متن کامل

Multi-Layer Model Based on Multi-Scale and Multi-Feature Fusion for SAR Images

A multi-layer classification approach based on multi-scales and multi-features (ML–MFM) for synthetic aperture radar (SAR) images is proposed in this paper. Firstly, the SAR image is partitioned into superpixels, which are local, coherent regions that preserve most of the characteristics necessary for extracting image information. Following this, a new sparse representation-based classification...

متن کامل

Multi-Scale Convolutional Architecture for Semantic Segmentation

Advances in 3D sensing technologies have made the availability of RGB and Depth information easier than earlier which can greatly assist in the semantic segmentation of 2D scenes. There are many works in literature that perform semantic segmentation in such scenes, but few relates to the environment that possesses a high degree of clutter in general e.g. indoor scenes. In this paper, we explore...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Optical and Quantum Electronics

سال: 2021

ISSN: ['1572-817X', '0306-8919']

DOI: https://doi.org/10.1007/s11082-021-02952-6