Sorting gangue from raw coal is an essential concern in coal mining engineering. Prior to separation, the location and shape of the gangue should be extracted from the raw coal image. Several approaches regarding automatic detection of gangue have been proposed to date; however, none of them is satisfying. Therefore, this paper aims to conduct gangue segmentation using a U-shape fully convolutional neural network (U-Net). The proposed network is trained to segment gangue from raw coal images collected under complex environmental conditions. The probability map outputted by the network was used to obtain the location and shape information of gangue. The proposed solution was trained on a dataset consisting of 54 shortwave infrared (SWIR) raw coal images collected from Datong Coalfield. The performance of the network was tested with six never seen images, achieving an average area under the receiver operating characteristics (AUROC) value of 0.96. The resulting intersection over union (IoU) was on average equal to 0.86. The results show the potential of using deep learning methods to perform gangue segmentation under various conditions.

Pixel-level crack detection is crucial in pavement performance assessment. Current deep learning-based detection methods first encode input images by multi-scale feature maps, then decode them to the output that has the same size as input. This process will lose detailed crack information. To tackle this problem, this paper proposed a novel network architecture, Enhanced High-Resolution Semantic Network (EHRS-Net), to maintain and enhance detailed information of the feature maps through convolution procedure, thus, improving the overall crack detection accuracy. The contributions of this paper are: (1) Proposed Resolution Maintain Flow (RMF), which is featured by three different semantic representation extraction flows in parallel with semantic information exchange; (2) Proposed Stacked Atrous Spatial Pyramid Pooling (SASPP) module to enhance the output of the semantic features; (3) Developed a new hybrid loss function to fit proposed network architecture. The proposed methods are evaluated on two pavement crack datasets: an expanded public crack forest dataset (CFD-ex) and a new dataset called HRSD (high-resolution semantic dataset). Comprehensive comparative experiments proved the superiority of the proposed method for pavement crack detection (93.353 % mPA (mean pixel accuracy) and 78.328% mIoU (mean intersection over union) on CFD-ex; 77.159% mIoU on HRSD), especially for tiny cracks and noised pavement cracks.

In order to measure the texture depth of asphalt pavement automatically and accurately, a data set generated by a real-time laser pavement texture meter (RLPTM) was realized, and a calculation method was developed. Three groups of experiments were designed and conducted to calibrate the measure results of the electric sand patch method (ESPM) to the ASTM sand patch method and compare the performance of the RLPTM with the ESPM. Experimental results showed that the correlation coefficient of the ESPM and sand patch method have reached up to 0.9830, indicating a good correlation. The RLPTM method had higher resolution (more significant figures) and better repeatability (lower coefficients of variation) than ESPM. The correlation coefficient between RLPTM and ESPM was 0.9207, demonstrating the good repeatability of the overall measurements. The absolute errors of the results obtained by RLPTM and ESPM were within 0.3 mm. Further analysis indicated that results of RLPTM were slightly over predicted (sl...

Immediate and accurate detection of foreign metal objects (FMOs) in clothing products is important for guaranteeing human safety. This article proposes an online detection approach based on deep learning, which is suitable for detecting small FMOs from X-ray images of clothing packages. A conveyor belt X-ray scanning system is developed for image collection. The X-ray images are preprocessed by using the morphological erosion operation to improve the accuracy of FMOs detection. These images are then down-sampled to reduce the computation cost. Feature pyramid network (FPN) is adopted for aggregating feature maps with different resolutions, which proved to be effective for small FMOs detection. The stochastic gradient descent (SGD) is used to optimize a multitask loss. The trained model was tested offline on 200 X-ray images, which achieved precision = 0.999, recall = 0.988, F1-score = 0.993, and AP = 0.946. Compared to original Faster region-based convolutional neural networks (R-CNN), the proposed method significantly improved the performance for small FMOs detection in terms of precision and recall rate.

The existing pavement crack detection algorithms are all dealing with detection and segmentation separately, ignoring the feature correlation between the bounding box coordinates and mask information, and there are still problems such as low crack detection accuracy, incomplete detection, and segmentation fracture in practical application. In view of the above situation, this paper took the detection bounding box coordinates and mask information as multimodal features of the same pavement crack area, and proposed a One-Stage MFFNet (Multimodal Feature Fusion Network), which improved the pavement crack detection accuracy and segmentation integrity obviously. And experimental results of different models were compared on self-collected datasets and two public datasets (CFD and CRACK500) respectively. Compared with Mask R-CNN, the average detection accuracy and average segmentation accuracy were improved by 2.6% and 4.7%, respectively. Compared with the optimised model of RDSNet, the detection accuracy and processing speed were improved by 1.8% and 2FPS, respectively. In addition, MFFNet had significantly improved the integrity of pavement crack segmentation results. The results showed that the proposed MFFNet model achieved the best detection and segmentation accuracy, and was an effective and high-precision pavement crack detection model.