Resumen
This study considered heated metal mark attribute recognition based on compressed convolutional neural networks (CNNs) models. Based on our previous works, the heated metal mark image benchmark dataset was further expanded. State-of-the-art lightweight CNNs models were selected. Technologies of pruning, compressing, weight quantization were introduced and analyzed. Then, a multi-label model training method was devised. Moreover, the proposed models were deployed on Android devices. Finally, comprehensive experiments were evaluated. The results show that, with the fine-tuned compressed CNNs model, the recognition rate of attributes meta type, heating mode, heating temperature, heating duration, cooling mode, placing duration and relative humidity were 0.803, 0.837, 0.825, 0.812, 0.883, 0.817 and 0.894, respectively. The best model obtained an overall performance of 0.823. Comparing with traditional CNNs, the adopted compressed multi-label model greatly improved the training efficiency and reduced the space occupation, with a relatively small decrease in recognition accuracy. The running time on Android devices was acceptable. It is shown that the proposed model is applicable for real time application and is convenient to implement on mobile or embedded devices scenarios.