# Heated Metal Mark Attribute Recognition Based on Compressed CNNs Model
A medium-scale CNNs, \(AlexNet\), proposed by [PERSON] et al. [14], won ImageNet 2012 competition by significant promotion over non-deep learning methods. More powerful models, e.g., \(ZFNet\), \(VGGNet\), \(Inception\) and \(ResNet\), etc., are designed successively by [PERSON] et al. [15; 16; 17; 18]. They improve CNNs by using more layers, small convolutional filters, flexible convolutional filter size, combined width and depth of model, and optimal and robust model training methods. \(ResNet\) has excellent performance of Top-5 error (3.57%) and it outperformed humans for the first time in the ImageNet 2015 classification task. \begin{table}
\begin{tabular}{c c} \hline
**Color** & **Heating Temperature** \\ \hline dark purple & 300 \({}^{\circ}\)C \\ sky blue & 350 \({}^{\circ}\)C \\ brown & 450 \({}^{\circ}\)C \\ dark red & 500 \({}^{\circ}\)C \\ orange & 650 \({}^{\circ}\)C \\ light yellow & 1000 \({}^{\circ}\)C \\ white & 1200 \({}^{\circ}\)C \\ \hline \end{tabular}
\end{table} 
Table 1: Relationship between color change of ferrous metal and heating temperature. The heating duration time is set with 30 min. Some relevant works are reported. A rail surface defects type recognition method was introduced based on CNNs model by [PERSON] et al. [19]. The model contains three convolutional layers, three max-pooling layers and two fully connected layers. It collects and labels 24,408 object images. A bearing fault diagnosis method is proposed for 10-type fault classification based on CNNs and an improved Dempster-Shafer algorithm by [PERSON] et al. [20]. The CNN model used in this work contains only three convolutional layers and one fully connected layer. By model ensemble, the final result is combined with various evidence. A steel defect area characterization method was proposed by [PERSON] [21], utilizing a magnetic multi-sensor matrix transducer. The basic model has three convolutional layers, three max-pooling layers and one fully connected layer. Three combined models are adopted for classification. In total, 35,000 simulated images are generated in this work. A hot-rolled steel sheet surface defect classification was designed by [PERSON] et al. [22]. Eight surface defect types are defined and 14,400 sample images are used. A civil infrastructure damage detection method was designed by [PERSON] et al. [23]. The structure of the model used in this method is the same as in Ref. [21]. Small patches are cut with manually annotated crack or intact and there are 40,000 sample images in the dataset. A structural surface damage detection method is proposed based on _faster r-cnn_ in Ref. [24]. Five types of surface damage atr defined and \(ZFNet\) os selected as backbone model. In total, 2366 images are collected as the dataset in this study. These works are similar to ours. However, they only model applications and simple CNN model structures are used. Models containing fewer than eight layers in Ref. [19; 20; 21; 22; 23] are used and \(ZFNet\) is adopted in Ref. [24]. The CNN model structures are relatively small and state-of-the-art models are not adopted. On the other hand, complete experimental evaluation and analysis are needed, including of training parameters, various CNN structures, etc. Our previous work performs a case study on heated metal attribute recognition based on CNNs [25]. We analyzed and selected seven heated metal attributes. Raw image set was generated with special capture devices (vacuum resistance furnace, muffle furnace, and gasoline burner; test chamber with constant temperature and humidity; and microscope) and a benchmark dataset was organized (900 image samples, each labeled with seven attributes). The relationship between attributes and mark image were trained with state-of-the-art CNNs models (_Inception-v4_, _Inception-v3_, _ResNet_, \(VGG16\), etc). Experimental evaluations were conducted according to various model structure, batch size, data augmentation, and training algorithms. This work is a continued research.