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In order to solve the problem of false alarms and missed alarms in pipeline gas leakage detection using a single sensor，timely warning and feedback of leakage status，a multi-source data fusion pipeline leakage detection method based on cross-attention was proposed. Firstly，the pre-trained ShuffleNetV2 model was used to extract spatial features from thermal imaging data. Then，a 1DCNN BiGRU model was constructed by combining a one-dimensional CNN (1DCNN) and a bidirectional gated recurrent unit (BiGRU) to extract temporal features from gas sensors. Finally，cross-attention was used to capture the spatiotemporal correlation of the data and obtain the feature representations of the two data sources. The residual method was used to connect the features and input them into the classification layer to obtain the recognition results. The results show that the constructed SCGA model has a gas recognition accuracy of 99.22%，and the loss value fluctuates between 0-0.04. Compared with support vector machines (SVM)，1DCNN，and BiGRU models that only use gas sensor data，the accuracy is improved by at least 4.12%. Compared with MobileNetV3，ShuffleNetV2，and ResNet18 models that only use thermal image sensor data，the accuracy is improved by at least 1.14%. Compared with the multi-source data fusion model SCG，which simply connects temporal and spatial features，the accuracy is improved by 1%. It was verified that the SCGA model has high accuracy.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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