In this paper, we propose a multi-scale receptive field detection network(MS-RFDN), a one-stage approach to detect objects of different scales in the image. The proposed network combines predictions of different scales from feature maps of different scales and receptive fields. To generate s ...
MRFA-Net: Multi-Scale Receptive Feature Aggregation Network for Cloud and Shadow Detectiondoi:10.3390/rs16081456MATRIX decompositionREMOTE sensingFEATURE extractionIMAGE processingCOMPUTATIONAL complexityThe effective segmentation of clouds and cloud shadows is crucial for surface feature extraction, c...
In this article, a novel Multi-Scale Feature Progressive Fusion Network (MFPF-Net) is proposed for remote sensing image CD, which aims to fully fuse bi-temporal remote sensing images, exchange feature information, promote information propagation and achieve better detection results. In MFPF-Net, ...
In the future, we consider integrating the automatic detection and segmentation of COVID-19, and conduct research on the automatic diagnosis system of COVID-19.Similar content being viewed by others Multi-scale input layers and dense decoder aggregation network for COVID-19 lesion segmentation from...
Firstly, we redesign the feature extractor by adopting Concatenated ReLU and Inception module, which can increases the variety of receptive field size. Then, the detection is preformed by two sub-networks: a multi-scale object proposal network (MS-OPN) for object-like region generation from ...
1. 多比例特征图检测(Multi-scale featuremaps for detection),我们在基网络最后增增加了卷积特征层。这些层按照大小减少的次序连接,能够进行多尺度预测。 2. 卷积检测预测器(Convolutionalpredictors for detection)每个添加的特征层或者现存的来自基网络的特征层,在使用一个卷积滤波器(convolutional filters)集合的情况下...
Depth-induced Multi-scale Recurrent Attention Network for Saliency Detection Yongri Piao Wei Ji Jingjing Li Miao Zhang∗ Huchuan Lu Dalian University of Technology, China yrpiao@dlut.edu.cn, {jiwei521,lijingjing}@mail.dlut.edu.cn, {miaozhang,lhchuan}@dlut.e...
Figure 3. The structure of a multi-scale fusion residual block (MSRB). The utilization of large convolution kernels in a network is associated with an increased receptive field, leading to the detection of more intricate features. However, these large kernels are high in computational complexity...
Table 1. The backbone network for feature extraction uses the following parameters. 3.3. Multi-Scale Receptive Field Detection Head The detection head in the Dynamic Scale-Aware Head YOLO series typically comprises a 3×33×3 convolutional layer followed by a 1×11×1 convolutional layer. Due ...
[28]: R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in CVPR, 2014, pp. 580-587. [29]: F. Yu and V. Koltun, “Multi-scale context aggregation by dilated convolutions,” arXiv preprint arXiv:...