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摘要: 為了解決傳統人工方法對廢鋼分類評級人為因素干擾大且效率低下等問題，提出基于擠壓?激勵（Squeeze?Excitation，SE）注意力機制構建廢鋼分類評級的深度學習網絡模型，并對采集到的廢鋼卸載過程圖像進行模型訓練和驗證。首先，搭建物理尺寸比例為1∶3廢鋼質量查驗物理模型，采用高分辨率視覺傳感器模擬采集貨車卸載廢鋼作業場景下不同廢鋼的形貌特征；然后，對采集到的廢鋼圖像使用跨階段局部網絡進行特征提取，利用空間金字塔結構解決特征丟失問題，采用注意力機制關注通道間的相關性；最后，在包含7個標簽分類的兩個數據集進行模型訓練與驗證。實驗表明：該模型能夠有效地對不同級別的廢鋼進行自動評級判定，全類別準確率達到83.7%，全類別平均精度為88.8%，在準確性方面相比于傳統人工驗質方法具有顯著優勢，解決了廢鋼入庫過程中質量評價的公正性難題。Abstract: Not only is scrap steel an indispensable ferritic raw material for the modern steel industry, but it is also the only green raw material that can replace iron ore in large quantities. The quality of the scrap steel directly affects the quality of molten steel, which makes it necessary to sort and grade scrap steel before it enters the furnace. Most iron and steel enterprises determine the grade of scrap steel mainly by visual inspection and caliper-based measurements by quality management personnel. As a result, this process is prone to human errors and low efficiency. Therefore, given that the major challenges of scrap inspection include the many categories of scrap, complex actual detection scenarios, and challenges in manual system connection, a deep learning network model CSSNet was proposed for scrap classification and rating based on the Squeeze-Excitation (SE) attention mechanism, and images of the scrap unloading process were collected for model training and validation. First, a 1∶3 physical model of scrap steel quality inspection was built to simulate this process. High-resolution visual sensors were used to collect images of diverse types of scrap steel in the scene of trucks unloading scrap steel. Then, a cross-stage local network was used to extract the features of the collected scrap images, the spatial pyramid structure was used to solve the problem of feature loss, and the attention mechanism was used to focus on the correlation between channels and retain the channel with the most feature information. Finally, model training and validation were done using two datasets containing seven labels for classification. In the model prediction stage, the constructed scrap steel quality inspection model CSSNet was used to judge the scrap steel category and quality to verify the accuracy and detection efficiency of the model. Based on the self-made scrap steel validation dataset, its performance was compared with mainstream single-stage object detection packages such as YOLOv4, YOLOv5s, and the two-stage object detection model Faster R-CNN. The model was found to be able to effectively rate different grades of scrap steel, with the classification accuracy rate of all categories has reached 83.7% and an mAP value of 88.8%. The performance of the CSSNet model is better than the other three target detection models. CSSNet can not only fully meet the needs of the actual production applications in terms of accuracy, real-time performance, and identification and rating efficiency but also surpass the traditional manual scrap quality inspection method, address multiple issues in the evaluation of scrap steel quality, and realize automated scrap steel quality testing.
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圖 5 模型在HK_S和HK_L數據集上loss值變化圖. (a) HK_S數據集，batch-size=16; (b)為HK_L數據集，batch-size=16; (c) HK_S數據集（添加SE注意力）; (d) HK_L數據集（添加SE注意力）
Figure 5. Changes in the loss value of the model on the HK_S and HK_L datasets: (a) HK_S dataset, batch-size=16; (b) HK_L dataset, batch-size=16; (c) HK_S dataset (add SE attention); (d) HK_L dataset (add SE attention)
圖 6 模型加入SE注意力機制前后的表現效果對比. (a)未加SE注意力機制; (b)添加SE注意力機制
Figure 6. Comparison of performance effects before and after adding the SE attention mechanism into the model: (a) no SE attention mechanism; (b) add SE attention mechanism
圖 7 模型檢測效果圖. (a)為未檢測廢鋼圖像; (b)檢測后的廢鋼圖像
Figure 7. Model detection renderings: (a) the undetected scrap image; (b) the detected scrap image
圖 8 各類別評價指標曲線. (a) P?R曲線; (b) F1曲線; (c) R曲線; (d) P曲線
Figure 8. Evaluation index curve of each category: (a) P–R curve; (b) F1 curve; (c) R curve; (d) P curve
表 1 HK_S、HK_L數據集
Table 1. HK_S and HK_L datasets
Datasets Images Labels Training images Training labels Validation images Validation labels HK_S 139 6297 125 5750 14 547 HK_L 278 12592 250 11388 28 1204 
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表 2 HK_S和HK_L數據集各類別標簽數量
Table 2. Number of labels for each category in HK_S and HK_L datasets
Label category HK_S labels HK_S training labels HK_L labels HK_L training labels <3 mm 92 57 184 164 3?6 mm 327 319 654 598 >6 mm 4799 4393 9596 8668 Galvanized 365 337 730 663 Greasy dirt 196 173 392 359 Paint 126 89 252 233 Inclusion 392 382 784 703
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表 3 正例與負例
Table 3. Positive and negative
Type P (Positive) N (Negative) T (True) True positive (TP) True negative (TN) F (False) False positive (FP) False negative (FN)
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表 4 HK_S數據集模型評價指數
Table 4. HK_S dataset model evaluation index
Model Batch-size Epoch F1 mAP Yolov5s 8 200 0.48 0.506 CSP+SPP 8 200 0.48 0.552 CSP+SPP+SE 8 200 0.60 0.642 Yolov5s 16 200 0.64 0.646 CSP+SPP 16 200 0.63 0.665 CSP+SPP+SE 16 200 0.71 0.719 Yolov5s 32 200 0.68 0.684 CSP+SPP 32 200 0.66 0.709 CSP+SPP+SE 32 200 0.70 0.720 CSP+SPP+SE 32 300 0.75 0.754
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表 5 HK_L數據集模型評價指數
Table 5. HK_L dataset model evaluation index
Model Batch-size Epoch F1 mAP Yolov5s 8 200 0.61 0.641 CSP+SPP 8 200 0.69 0.699 CSP+SPP+SE 8 200 0.75 0.755 Yolov5s 16 200 0.79 0.792 CSP+SPP 16 200 0.79 0.802 CSP+SPP+SE 16 200 0.83 0.833 Yolov5s 32 200 0.82 0.805 CSP+SPP 32 200 0.84 0.839 CSP+SPP+SE 32 200 0.87 0.868 CSP+SPP+SE 32 400 0.87 0.888
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表 6 不同網絡模型檢測結果比較
Table 6. Comparison of detection results of different network models
Model Datasets mAP/% YOLOv4 HK_S 60.0 YOLOv5s HK_S 50.6 Faster R-CNN HK_S 50.9 CSSNet HK_S 64.2 YOLOv4 HK_L 68.1 YOLOv5s HK_L 64.1 Faster R-CNN HK_L 64.1 CSSNet HK_L 75.5
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表 7 各類別驗證集的表現情況
Table 7. Performance under each category of the validation set
Class Images Labels P/% R/% AP/% <3 mm 28 20 100 79.8 86.6 3–6 mm 28 56 89.5 91.2 93.7 >6 mm 28 928 91.2 83.7 88.8 Galvanized 28 67 87.3 94.0 94.0 Paint 28 81 92.0 85.2 91.9 Greasy dirt 28 33 83.1 69.7 76.7 Inclusion 28 19 100 81.6 89.8
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