L-DeeplabV3+: a lightweight semantic segmentation algorithm for complex scene perception

Current semantic segmentation algorithms are often burdened by high computational complexity and inadequate boundary localization accuracy in complex scenarios of industrial manufacturing. To achieve lightweight semantic segmentation with high accuracy, we propose a semantic segmentation algorithm named L-DeepLabV3+. First, a CB-MobileNetV3 is proposed as the backbone network, which reduces the computational burden. Second, we propose a DSCAASPP module to compensate for the loss of some detailed information in the encoder and enhance the perception of boundary information. Within the DSCAASPP module, we propose dilated separable convolution to address the issue of dilated convolutional meshing. Finally, we introduce the Haar Wavelet Transform with Deepwise Separable Convolution (HWTS) module as a replacement for the 3×3 convolution in the decoder. It consists of a deepwise separable convolution and Haar wavelet transform module, which further reduces the number of model parameters and improves the feature representation in the decoder. Experimental results demonstrate that L-DeepLabV3+ achieves an 82. 7% mIoU on the PASCAL VOC2012 dataset, utilizing only 2. 73 million parameters and a floating-point computation amounting to 43. 015 G. The L-DeepLabV3+ model has ∼4% of the number of parameters compared to the traditional DeepLabV3+, resulting in a significant reduction in computational burden. Furthermore, L-DeepLabV3+ outperforms common semantic segmentation algorithms while using fewer model parameters and enhancing segmentation performance. Moreover, L-DeepLabV3+ exhibits excellent performance in detecting defects in industrial ceramic substrates.