Improved brain-inspired DeeplabV3+ algorithm for complex scenes

In response to the issues of spatial detail loss and insufficient feature extraction in the existing DeeplabV3+ model of complex scenes, we employ the visual processing mechanisms of the human brain and propose an improved brain-inspired DeeplabV3+ algorithm for complex scenes. First, MobileNetV2 is used to replace Xception as the backbone network, and the feature extraction process is further optimized by the coordinate attention model so that the model can capture spatial information more efficiently and avoid the loss of details in complex scenes. Second, the continuous-coupled neural network model is applied after each atrous convolution, and the characteristics of a brain-like neural network are used to capture more spatial context information and enhance the feature extraction ability of the model. Finally, the simple attention module is added after the shallow features of the backbone network, the extraction ability of key features is further enhanced by weighting the input features, and the clarity of the segmentation boundary is improved. To rigorously evaluate the performance of the proposed algorithm, we conduct extensive experimental validation on two publicly available datasets: the PASCAL VOC2012 and DLRSD datasets. The experimental results show that the mean intersection over union (mIoU) of the proposed algorithm on the PASCAL VOC2012 dataset reaches 73.75%, which is 2.37% higher than that of the original DeeplabV3+ algorithm, and the mIoU on the DLRSD dataset reaches 64.88%, that is, an increase of 3.36%. At the same time, the mean pixel accuracy achieves 83.6% and 80.8%, respectively, demonstrating significant superiority compared with other classical algorithms. In addition, ablation experiments verify the fusion effectiveness of the improved modules and prove that the proposed algorithm can effectively improve the segmentation accuracy and detail performance in complex scenes. We not only provide a new solution for image semantic segmentation in complex scenes but also contribute an important theoretical and practical reference for the design of lightweight and high-precision semantic segmentation models.