CFR-ICL: Cascade-Forward Refinement with Iterative Click Loss for Interactive Image Segmentation

The click-based interactive segmentation aims to extract the object of interest from an image with the guidance of user clicks. Recent work has achieved great overall performance by employing the segmentation from the previous output. However, in most state-of-the-art approaches, 1) the inference stage involves inflexible heuristic rules and a separate refinement model; and 2) the training cannot balance the number of user clicks and model performance. To address the challenges, we propose a click-based and mask-guided interactive image segmentation framework containing three novel components: Cascade-Forward Refinement (CFR), Iterative Click Loss (ICL), and SUEM image augmentation. The proposed ICL allows model training to improve segmentation and reduce user interactions simultaneously. The CFR offers a unified inference framework to generate segmentation results in a coarse-to-fine manner. The proposed SUEM augmentation is a comprehensive way to create large and diverse training sets for interactive image segmentation. Extensive experiments demonstrate the state-of-the-art performance of the proposed approach on five public datasets. Remarkably, our model achieves an average of 2.9 and 7.5 clicks of NoC@95 on the Berkeley and DAVIS sets, respectively, improving by 33.2% and 15.5% over the previous state-of-the-art results. The code and trained model are available at https://github.com/TitorX/CFR-ICL-Interactive-Segmentation

Small Object Detection Based on Two-Stage Calculation Transformer

Despite the current small object detection task has achieved significant improvements, it still suffers from some problems. For example, it is a challenge to extract small object features because of little information in the scene of small objects, which may lose the original feature information of small object, resulting in poor detection results. To address this problem, this paper proposes a two-stage calculation Transformer (TCT) based small object detection network. Firstly, a two-stage calculation Transformer is embedded in the backbone feature extraction network for feature enhancement. Based on the traditional Transformer values computation, multiple 1D dilated convolutional layer branches with different feature fusions are utilized to implement global self-attention for the purpose of improving the feature representation and information interaction. Secondly, this paper proposes an effective residual connection module to improve the low-efficiency convolution and activation of the current CSPLayer, which helps to advance the information flow and learn more rich contextual details. Finally, this paper proposes a feature fusion and refinement module for fusing multi-scale features and improving the target feature representation capability. Quantitative and qualitative experiments on PASCAL VOC2007+2012 dataset, COCO2017 dataset and TinyPerson dataset show that the proposed algorithm has better ability of target feature extraction and higher detection accuracy for small target detection, compared with YOLOX

Net Phosphorus Requirements of Dorper×Thin-tailed Han Crossbred Ram Lambs

A comparative slaughter trial was conducted to estimate the phosphorus (P) requirement for maintenance and growth of crossbred lambs of Dorper with a Chinese indigenous sheep breed, thin-tailed Han sheep. Thirty-five Dorper×thin-tailed Han crossbred, noncastrated ram lambs (20.3±0.22 kg of shrunk body weight (SBW)) were used. Seven lambs were randomly chosen and slaughtered at 20 kg SBW as the baseline group for measuring initial body composition. Another seven lambs were also randomly chosen and offered a pelleted mixed diet for ad libitum intake and slaughtered at 28 kg SBW. The remaining 21 sheep were randomly divided into 3 groups with 7 sheep each and subject to the same diet of either 70 or 40% of ad libitum intake. The 3 groups were slaughtered when the sheep fed ad libitum reached 35 kg of SBW. Body P contents were determined after slaughter. The results showed that the net P requirement for maintenance was 30.0 mg/kg of empty body weight (EBW) or 23.4 mg/kg body weight (BW), and the P requirement for growth decreased from 5.3 to 5.0 g/kg of EBW gain as the lamb grew from 20 to 35 kg. The net P requirement for growth of Dorper×thin-tailed Han crossbred ram lambs was lower than that of sheep adopted by the American nutritional system

Thermal Decomposition Behaviour of Foundry Sand for Cast Steel in Nitrogen and Air Atmospheres

Sand casting is the most widely used casting technique, known for ages, even since ancient times. The main goal of this study was to determine the thermal decomposition behaviour of foundry sand for cast steel. We first tested the basic properties of foundry sand, including its proximate analysis, chemical composition, and particle size characteristics; we next monitored the thermal decomposition behaviour of foundry sand for cast steel via simultaneous thermal analysis. We focused on the mass loss of foundry sand for cast steel at different heating rates in nitrogen and air atmospheres. We adopted a novel method to calculate the volatile release characteristic index of foundry sand. The volatile content of foundry sand for cast steel was very low, so the volatile release characteristic index of the sand could not be strictly calculated according to this concept. We calculated the thermal decomposition kinetics parameters of foundry sand, namely, the activation energy and preexponential factor, under kinetics theory. To thoroughly test the fitting effect, we conducted a single-factor analysis of variance on the source of error. The results showed that the independent variable has a significant influence on the dependent variable and that the fitting equation we selected is feasible and effective

A Study on a Knowledge Graph Construction Method of Safety Reports for Process Industries

There are some representative reports in industrial safety engineering, such as the Hazard and Operability Analysis and Pre-Hazard Analysis; however, a large amount of industrial safety knowledge in the report has not been fully explored. In order to reuse and release the value of industrial safety knowledge, this paper constructs a new industrial safety knowledge extraction framework. The framework combines the asset management shell to summarize the knowledge concept entities of machine description language and model description language. According to the safety report template, the framework also constructs a new industrial safety knowledge-mapping standard structure. Specifically, firstly, considering that the knowledge structure of safety reports is different in different processes of the process industry, this paper innovatively proposes a general industrial safety knowledge-mapping standard structure, which provides a practical solution for the integration of industrial knowledge representation problems in different processes. Secondly, based on the research progress of named entities, this paper presents an industrial named entity extraction method (INERM) for the process industry. This method designs an entity weight model to calculate the entity weight of each sentence, and adds part-of-speech weight to improve the entity extraction algorithm, which alleviates the problem that the existing entity extraction methods cannot reasonably use the semantic information and context of word. Finally, we construct a triple of industrial safety knowledge based on the rules and store it in Neo4j. In this paper, four semantic-type templates and five semantic relation templates are constructed based on the new industrial safety knowledge map standardization construction process of the process industry. The comparative experiments show that the accuracy of the INERM on the test set is improved by 17 percentage points on average compared with other key entity extraction algorithms. A total of 1329 entities are constructed in the directional application example of the fluid transportation process, which provides a large number of references for the safety of the fluid transportation process and is more conducive to improving the safety guarantee of the fluid transport process