Deeper feature integration network for salient object detection of strip steel surface defects

The performance of the salient object detection of strip surface defects has been promoted largely by deep learning based models. However, due to the complexity of strip surface defects, the existing models perform poorly in the challenging scenes such as noise disturbance, and low contrast between defect regions and background. Meanwhile, the detection results of existing models often suffer from coarse boundary details. Therefore, we propose a novel saliency model, namely an Edge-aware Multi-level Interactive Network, to detect the defects from the strip steel surface. Concretely, our model adopts the U-shape architecture where the two crucial points are the interactive feature integration and the edge-guided saliency fusion. Firstly, except the skip connection that combines the same stage of encoder and decoder, we deploy another connection, where the features from adjacent levels of encoder are transferred to the same stage of decoder. By this way, we are able to provide an effective fusion of multi-level deep features, yielding a well depiction for defects. Secondly, to give well-defined boundaries for prediction results, we add the edge extraction branch after each decoder block, where the progressive feature aggregation endows the edge with precise details and complete object cues. Meanwhile, together with the edge extraction branches, we deploy the saliency prediction branch at each decoder stage. After that, coupled with the fine edge information, we fuse all outputs of saliency prediction branches into the final saliency map, where the edge cue steers the saliency result to pay more attention to the boundary details. Following this way, we can provide a high-quality saliency map which can accurately locate and segment the defects. Extensive experiments are performed on the public dataset, and the results prove the effectiveness and robustness of our model which consistently outperforms the state-of-the-art models.

The automatic detection and recognition of steel surface defects is very important in the steel manufacturing industry based on the classification of defect types using artificial intelligence. Deep learning plays an important role to decrease the defection rate in the steel manufacturing industry. Defects in the surface of steel have a serious effect on the quality of steel. Previous research for the NEU-DET dataset has shown that there are some limitations, such as low detection accuracy. In this paper Feature Pyramid Network (FPN) + Resnet neural network has been proposed for detecting the overlapping and multiple defects in the complex background for improving the defect detection rate. The histogram and edge detection approach is used for feature extraction for the classification of defects. The final result shows that the proposed FPN + Resnet model trained with good steel surface defect detection performance, and the dice score is 0.7963, IoU score is 0.6818 and losses is 0.0522 which is very low as compared to others. The proposed detection approach can efficiently detect minor targeted defects on the steel surface, which can be used as a benchmark for automated steel surface defect identification.

Detection of surface defects in strip steel is crucial in industrial automation to ensure steel quality. Recently, learning-based detection methods have gained prominence among researchers for their robust capabilities. However, accurately delineating clear area boundaries remains a challenge, particularly in the complex backgrounds of steel surfaces. In this paper, we propose the edge-aware Interactive Refinement Network (EAIRNet) to address this challenge. EAIRNet comprises three core modules: a Multiscale Feature Extractor, a Sal-Edge Interactive Module, and a regional attention guided module (RAGM). Firstly, our network extracts multiscale features using ResNet from the steel surface environment. Subsequently, features from encoder’s stage 3–5 are integrated to derive edge features. The SEIM facilitates interaction between salient and edge features in the decoder stage, thereby enhancing the network’s focus on defect area boundaries and refining detection precision. Additionally, we introduce the RAGM to improve the focus of shallow decoder layers on defect areas. Our EAIRNet’s effectiveness is proven through comprehensive experimental results on the public dataset named SD-Saliency-900, in comparison to state-of-the-art methods.

An efficient re-parameterization feature pyramid network on YOLOv8 to the detection of steel surface defect

Surface defects have a serious detrimental effect on the quality of steel. To address the problems of low efficiency and poor accuracy in the manual inspection process, intelligent detection technology based on machine learning has been gradually applied to the detection of steel surface defects. An improved YOLOv8 steel surface defect detection model called YOLOv8-MGVS is designed to address these challenges. The MLCA mechanism in the C2f module is applied to increase the feature extraction ability in the backbone network. The lightweight GSConv and VovGscsp cross-stage fusion modules are added to the neck network to reduce the loss of semantic information and achieve effective information fusion. The self-attention mechanism is exploited into the detection network to improve the detection ability of small targets. Defect detection experiments were carried out on the NEU-DET dataset. Compared with YOLOv8n from experimental results, the average accuracy, recall rate, and frames per second of the improved model were improved by 5.2%, 10.5%, and 6.4%, respectively, while the number of parameters and computational costs were reduced by 5.8% and 14.8%, respectively. Furthermore, the defect detection generalization experiments on the GC-10 dataset and SDD DET dataset confirmed that the YOLOv8-MGVS model has higher detection accuracy, better lightweight, and speed.

Steel is widely used in the aerospace, machinery and automotive industries. Surface defects not only have a negative impact on the appearance of steel but also significantly reduce its wear resistance, high temperature resistance, corrosion resistance and fatigue strength. Therefore, the detection of steel surface defects is very important to improve the quality of steel production. The limited availability of surface defect samples in the industrial sector poses significant challenges for the accurate detection of defects in high-quality materials. In addition, the existing defect detection model is highly complex and not easy to deploy. To solve this problem, a lightweight defect detection network suitable for steel defects is proposed. The cross-layer feature fusion (CFF) in the design enables effective utilisation of multi-layer semantic features, facilitating the detection of small defects in steel. Secondly, a new loss function is designed to make up for the problems of small data volume and uneven data distribution. The experimental results demonstrate that the steel surface defect detection method proposed in this paper achieves the highest detection performance on widely used public datasets such as RSDDS, NEUS and NRSD-CR(test), while maintaining the lowest model complexity.

With the development of industrial economy, more and more enterprises use machine vision and artificial intelligence to replace manual detection. Therefore, the research of steel surface defect detection based on artificial intelligence is of great significance to promote the rapid development of intelligent factory and intelligent manufacturing system. In this paper, Yolov5 deep learning algorithm is used to build a classification model of steel surface defects to realize the classification and detection of steel surface defects. At the same time, on the basis of Yolov5, combined with the attention mechanism, the backbone network is improved to further improve the classification model of steel surface defects. The experiment shows that the Recall and mAP of improved Yolov5 perform better on the steel surface defect data set. Compared with Yolov5, the number of C3CA-Yolov5 parameters decreased by 13.02%, and the size of pt files decreased by 12.72%; the number of C3ECA-Yolov5 parameters decreased by 13.36%, and the size of pt files decreased by 13.22%.

Surface quality assessment of magnetic tile before mounting is extremely significant. At present, this task is mainly accomplished by experienced workers in industry, which exposes the drawbacks of low efficiency and high cost. To overcome these issues, an intelligent system is developed to perform this task, which appears to be an efficient and reliable substitute for human workers. In this article, deep learning technique is embedded into our system for automatic defect identification. However, conventional convolutional neural network (CNN) is not suitable for this classification task, since the input is a sample rather than a single image. To overcome this problem, an end-to-end CNN architecture is proposed, termed fusion feature CNN (FFCNN). FFCNN consists of three modules: feature extraction module, feature fusion module, and decision-making module. The feature extraction module is designed to extract features from different images. The feature fusion module is to fuse the features extracted by feature extraction module. The decision-making module is to predict the label by the fused features. Furthermore, an attention mechanism is introduced to focus on more representative parts and suppress less important information. Experimental results demonstrated that the developed system is effective and efficient for magnetic tile surface defect detection.

A pooling-based feature pyramid network for salient object detection

In this work, the creation of a dataset labeled in a pixel-wise manner for the uncommon domain of stain detection on patterned laundry is described. The unique properties of images in this dataset—stains are small and sometimes occur in large amounts—led to the creation of noisy labels. Indeed, the training of a fully convolutional neural network for salient object detection with this dataset revealed that the model predicts stains missed by human labelers. Thus, the reduction in label noise by adding overlooked regions with the help of the model’s predictions is examined in two different experiments. In the model-assisted labeling experiment, a simulation is ran where a human selects correct regions from the predictions. In the self-training experiment, regions of high certainty are automatically selected from the predictions. Re-training the model with the revised labels shows that model-assisted labeling leads to an average improvement in performance by 8.52%. In contrast, with self-training, the performance increase is generally lower (2.58% on average) and a decrease is even possible since regions of high certainty are often false positives.

In this paper, we propose a detection method for salient objects whose eyes are focused on gaze tracking; this method does not require a device in a single image. A network was constructed using Neg-Region Attention (NRA), which predicts objects with a concentrated line of sight using deep learning techniques. The existing deep learning-based method has an autoencoder structure, which causes feature loss during the encoding process of compressing and extracting features from the image and the decoding process of expanding and restoring. As a result, a feature loss occurs in the area of the object from the detection results, or another area is detected as an object. The proposed method, that is, NRA, can be used for reducing feature loss and emphasizing object areas with encoders. After separating positive and negative regions using the exponential linear unit activation function, converted attention was performed for each region. The attention method provided without using the backbone network emphasized the object area and suppressed the background area. In the experimental results, the proposed method showed higher detection results than the conventional methods.

Attention and boundary guided salient object detection

Given that existing salient object detection methods cannot effectively predict the fine contours of salient objects when extracting local or global contexts and features, we propose a novel contour self-compensated network (CSCNet) to generate a more accurate saliency map with complete contour. Unlike the common binary saliency detection, we reconstruct the salient object detection problem into a multi-classification problem of the background, the salient object, and the salient contour, where the salient contour is used as the third label for ground truth. Meanwhile, the image and its superpixel map are concatenated as the input of our network to add more edge information. Also, a penalty loss is proposed to restrict the spatial relationship between the background, objects, and their contours. Experimentally, we evaluate the proposed CSCNet on six benchmark datasets in both accuracy and efficiency and evaluate the attribute-based performance on the SOC dataset. Compared with 13 state-of-the-art algorithms, our CSCNet can detect salient objects more accurately and completely without adding too many convolutional layers and parameters.

Many existing salient object detection methods are dedicated to fusing features from different levels of a pre-trained convolutional neural network (CNN). However, these methods can easily lead to internal discontinuities within the salient objects because of unreasonable feature fusion strategies and short-range dependencies resulting from common convolution and pooling operations. In this paper, we propose a novel non-local duplicate pooling (NLDP) network to overcome these internal discontinuities. NLDP begins by removing the first few convolutional layers of a classic CNN, which have small receptive fields and require large amounts of calculation. A novel duplicate pooling module (DPM) is then used to generate richer and more detailed saliency maps. This is achieved by constructing a double-pathway that can integrating partial feature maps. Within the DPM, a non-local module (NLM) is used to obtain long-range dependencies. This enhances the internal continuities between the saliency maps. Comprehensive experiments conducted on six benchmark datasets have confirmed the increased effectiveness and detection speed of our method in relation to other salient object detection methods.

A Coordinated Interaction and Cross Enhancement Network for Salient Object Detection