Abstract: End-to-end stereo matching algorithms have become increasingly popular in stereo matching tasks due to their advantages in computational time and matching accuracy.However,feature extraction in such algorithms can result in redundant features,information loss,and insufficient multi-scale feature fusion,thereby increasing computational complexity and decreasing matching accuracy.To address these challenges,an improved ghost adaptive aggregation network(GAANET) is proposed based on the adaptive aggregation network(AANET),and its feature extraction module is improved to make it more suitable for stereo matching tasks.Multi-scale features are extracted in the G-Ghost phase,and partial features are generated through low-cost ope-rations to reduce feature redundancy and preserve shallow features.An efficient channel attention mechanism is implemented to allocate weights to each channel,and an improved feature pyramid structure is introduced to mitigate channel information loss in traditional pyramids and optimize feature fusion,thus enhancing information supplement for features across scales.The proposed GAANET model is trained and evaluated on the SceneFlow,KITTI2015,and KITTI2012 datasets.Experimental resultsdemons-trate that GAANET outperforms the baseline method,with accuracy improvements of 0.92%,0.25%,and 0.20%,respectively,while reducing parameter volume by 13.75% and computational complexity by 4.8%.

Key words: Stereo matching, Feature extraction, End-to-end stereo matching network, Attention module, Deep learning