A Lane Line Detection Method Based on Squeeze and Excitation Network

Liu, Yichen

doi:10.1109/mlise57402.2022.00031

Cited by 1 publication

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Through the weight distribution of the features extracted by the utilized neural network at different levels, the learning ability of the network is strengthened with a certain relationship. The squeeze operation of SENet [24] uses global average pooling to learn the dependencies between channels and uses multiple fully connected layers to reweight each channel through an excitation operation; thus, it can adaptively adjust the weights of different channels and improve the ability of the model to express feature information. OCNet [25] was proposed as a new semantic segmentation method that no longer performs prediction in a pixel-by-pixel manner but rather aggregates similar pixels.…”

Section: Applications Of Attention Mechanisms In Image Processing Modelsmentioning

confidence: 99%

Ancient mural segmentation based on multiscale feature fusion and dual attention enhancement

Cao,

Chen

et al. 2024

Herit Sci

View full text Add to dashboard Cite

To address the fuzzy segmentation boundaries, missing details, small target losses and low efficiency of traditional segmentation methods in ancient mural image segmentation scenarios, this paper proposes a mural segmentation model based on multiscale feature fusion and a dual attention-augmented segmentation model (MFAM). The model uses the MobileViT network, which integrates a coordinate attention mechanism, as the feature extraction backbone network. It attains global and local expression capabilities through self-attention, class convolution, and coordinate attention and focuses on location information to expand the receptive field and achieve improved feature extraction efficiency. An A_R_ASPP feature enhancement module is proposed for the attention-optimized residual atrous spatial pyramid pooling module. The module uses residual connections to solve the small target loss problem in murals caused by the excessive sampling rate of atrous convolution and uses a feature attention mechanism to adaptively adjust the feature map weight according to the channel importance levels. A dual attention-enhanced feature fusion module is proposed for multiscale decoder feature fusion to improve the mural segmentation effect. This module uses a cross-level aggregation strategy and an attention mechanism to weight the importance of different feature levels to obtain multilevel semantic feature representations. The model improves the mean intersection over union (MIoU) by 3.06% and the MPA by 1.81% on a mural dataset compared with other models. The model is proven to be effective at improving the segmentation details, efficiency and small target segmentation results produced for mural images, and a new method is proposed for segmenting ancient mural images.

show abstract

Section: Applications Of Attention Mechanisms In Image Processing Modelsmentioning

confidence: 99%