Low-altitude perception models pruning algorithm with irregular structures

Large neural networks are challenging to implement on terminal devices due to restricted processing resources, notwithstanding deep learning’s superior performance in low-altitude intelligent sensing. Pruning techniques reduce model complexity by eliminating redundant parameters, thereby improving real-time performance. However, existing methods primarily target regularly structured models and struggle to handle irregular structures generated by neural architecture search (NAS). To address this, a dependency graph-based pruning scheme for irregular structures is proposed to optimize model efficiency. The method first employs structural parsing techniques of computational graphs to automatically identify unique multi-level connection patterns in low-altitude perception models. Subsequently, it constructs an efficient parameter grouping module to precisely locate minimum functional units that can be completely removed. Finally, a cross-layer comparison global optimization strategy is adopted to implement unified evaluation and pruning of isomorphic substructures. This solution overcomes the technical constraints of structural adaptability when compared to current approaches, allowing global precision compression for irregular network architectures. This feature makes it especially appropriate for low-altitude perception scenarios that call for intricate spatial relationship processing. Experimental results demonstrate that the proposed irregular structure pruning method can effectively identify and perform group pruning on complex structures, including multi-branch connections, residual connections, and concatenations, in both NAS-Bench101 models and ResNet series networks. When implementing 50% compression rate network lightweighting on the CIFAR-10 dataset, the recognition accuracy drop does not exceed 1%.