Yin Zhenyu, Wang Zisong, Fan Chao, Wang Xiaohui, Qiu Tong
Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2023 Aug 3;23(15):6883. doi: 10.3390/s23156883.
Edge detection is a crucial step in many computer vision tasks, and in recent years, models based on deep convolutional neural networks (CNNs) have achieved human-level performance in edge detection. However, we have observed that CNN-based methods rely on pre-trained backbone networks and generate edge images with unwanted background details. We propose four new fusion difference convolution (FDC) structures that integrate traditional gradient operators into modern CNNs. At the same time, we have also added a channel spatial attention module (CSAM) and an up-sampling module (US). These structures allow the model to better recognize the semantic and edge information in the images. Our model is trained from scratch on the BIPED dataset without any pre-trained weights and achieves promising results. Moreover, it generalizes well to other datasets without fine-tuning.
边缘检测是许多计算机视觉任务中的关键步骤，近年来，基于深度卷积神经网络（CNN）的模型在边缘检测方面取得了人类水平的性能。然而，我们观察到基于CNN的方法依赖于预训练的骨干网络，并生成带有不需要的背景细节的边缘图像。我们提出了四种新的融合差分卷积（FDC）结构，将传统梯度算子集成到现代CNN中。同时，我们还添加了一个通道空间注意力模块（CSAM）和一个上采样模块（US）。这些结构使模型能够更好地识别图像中的语义和边缘信息。我们的模型在BIPED数据集上从头开始训练，没有任何预训练权重，并取得了有希望的结果。此外，它在不进行微调的情况下也能很好地推广到其他数据集。
