To address the high cost and low efficiency caused by manual monitoring and adjustment of equipment parameters in the production of cigarette filter rods, a filter rod quality inspection algorithm with multi-scale feature fusing attention mechanism is proposed. The algorithm uses one-dimensional convolutions of various sizes to obtain the multi-scale features of the filter rod, which greatly reduces the possibility of missed detection of local features. To further enhance the feature representation, this paper introduces an attention mechanism, enabling the model to focus on regions with richer feature information. Experimental results show that compared with five existing methods such as 1D convolutional neural network (1DCNN), back propagation (BP) neural network and recurrent neural network (RNN), the proposed algorithm shows superior performance on the test set, especially compared with the three methods of 1DCNN, BP neural network and extreme gradient boosting. Specifically, the accuracy of the model increases by 3.27%, 4.28% and 5.70%; the precision improves by 3.12%, 4.68% and 5.10%; the recall rate rises by 3.28%, 4.57% and 2.97%; and the F1-Score enhances by 3.20%, 4.13% and 4.33%, respectively. In general, the algorithm proposed in this paper not only increases the production efficiency of filter rods, but also reduces labor costs, demonstrating practical engineering value.
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