Abnormal Occupancy Grid Map Recognition using Attention Network

Deng, Fuqin; Feng, Huanqing; Liang, Mingjian; Feng, Qinghua; Yi, Ningbo; Yang, Yong; Gao, Yuan; Chen, Junfeng; Lam, Tsun Ip

doi:10.1109/icra46639.2022.9812106

Cited by 2 publications

(1 citation statement)

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“…But these methods ignore the linkage between global feature information and local feature information, which can afect the fusion of features and the generation of accurate attention maps. Deng et al built a csRSE module for occupancy grid map recognition [24], which contains a residual block for generating hierarchical features, followed by a channel SE block and a spatial SE block for adequate information extraction along the channel and space. To achieve more fexible computation allocation and content awareness, Zhu et al introduced the contentindependent sparsity into the attention mechanism and proposed the BiFormer which selectively attended to relevant tokens in an adaptive manner, without dispersing attention to other unrelated tokens [25].…”

Section: Related Workmentioning

confidence: 99%

CNFA: ConvNeXt Fusion Attention Module for Age Recognition of the Tangerine Peel

Deng,

Li,

et al. 2024

Journal of Food Quality

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Xinhui tangerine peel has valuable medicinal value. The longer it is stored in an appropriate environment, the higher its flavonoid content, resulting in increased medicinal value. In order to correctly identify the age of the tangerine peel, previous studies have mostly used manual identification or physical and chemical analysis, which is a tedious and costly process. This work investigates the automatic age recognition of the tangerine peel based on deep learning and attention mechanisms. We proposed an effective ConvNeXt fusion attention module (CNFA), which consists of three parts, a ConvNeXt block for extracting low-level features’ information and aggregating hierarchical features, a channel squeeze-and-excitation (cSE) block and a spatial squeeze-and-excitation (sSE) block for generating sufficient high-level feature information from both channel and spatial dimensions. To analyze the features of tangerine peel in different ages and evaluate the performance of CNFA module, we conducted comparative experiments using the CNFA-integrated network on the Xinhui tangerine peel dataset. The proposed algorithm is compared with related models of the proposed structure and other attention mechanisms. The experimental results showed that the proposed algorithm had an accuracy of 97.17%, precision of 96.18%, recall of 96.09%, and F1 score of 96.13% for age recognition of the tangerine peel, providing a visual solution for the intelligent development of the tangerine peel industry.

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Section: Related Workmentioning

confidence: 99%