Efficient Convolutional Neural Networks With PWK Compression for Gas Pipelines Leak Detection

The convolutional neural network (CNN) has achieved good performance for gas pipeline leak detection. However, the high computing cost hinders its application in embedded devices with limited hardware performance. To address this limitation, a model compression method called PWK is proposed, consisting of two parts: a pruning-optimizing iterative strategy and weight sharing combined with knowledge distillation (KD). The pruning-optimizing iterative strategy consists of pruning and training phases. In the pruning phase, the compact network is trimmed using a mask derived from virtually pruning the original network, aiming to obtain the original network’s essential structure. In the training phase, important features (IFs) extracted from the original network by the mask are transferred to the compact network through feature distillation (FD) to form a pruned network with low redundancy. KD was introduced into weight sharing, which can reduce weight storage to alleviate performance degradation. Finally, PWK compresses spectral enhancement CNN (SE-CNN) to an efficient CNN (SE-CNN-Lite) for gas pipeline leak detection. Experimental results show that the pruning-optimizing iterative strategy proposed in this article has higher performance than other state-of-the-art methods under similar compression ratios. Our SE-CNN-Lite has lower redundancy than other state-of-the-art lightweight networks and achieves the optimal detection accuracy for gas pipeline leaks in low signal-to-noise ratio environments.