关键词: 嵌套演化, 轻量化, 特征依赖, 膨胀率, DeepLabV3+

Abstract:

To address the problems of high complexity and low segmentation accuracy for certain classes in DeepLabV3+ caused by atrous convolutions with different dilation rates， an improved method that integrates Evolutionary Nested Receptive Field （ENRF） module with Adaptive Class-Channel Attention （ACCA） mechanism was proposed. In this method， the original Atrous Spatial Pyramid Pooling （ASPP） module was replaced by ENRF module， and ACCA mechanism was incorporated into the fused features， enabling continuous expansion of receptive field and more fine-grained feature representation， and reducing the number of parameters and computational overhead to enhance the model’s efficiency and lightweightness. Firstly， ACCA mechanism was constructed by combining channel-adaptive and class-adaptive attention mechanisms， which exploited inter-channel and inter-class feature dependencies to strengthen the representation of critical information in feature maps. Secondly， ENRF module was designed by introducing convolution kernels of different sizes and dilation rates， forming a nested evolutionary receptive field structure that gradually enlarged the receptive field to capture multi-scale contextual information and fine-grained boundary details. The improved method was compared with Fully Convolutional Network with 8s skip connections （FCN8s）， Pyramid Scene Parsing Network （PSPNet）， Unified Perceptual parsing Network （UPerNet）， Bilateral Segmentation Network Version 2 （BiSeNet V2）， Deep Feature Aggregation Network （DFANet）， and the original DeepLabV3+ in terms of FLOPs （FLoating-point OPerations）， parameter count， mean Intersection over Union （mIoU）， inference speed， and memory usage. Experimental results show that the improved DeepLabV3+ reduces parameters and FLOPs， accelerates inference， and improves segmentation performance.

Key words: nested evolution, lightweighting, feature dependency, dilation rate, DeepLabV3+