Object Detection for Cattle Gait Tracking

Accurate detection of lameness in dairy cattle with computer vision: A new and individualized detection strategy based on the analysis of the supporting phase

Cost benefit analysis of automatic lameness detection systems in dairy herds: A dynamic programming approach

Automatic lameness detection based on consecutive 3D-video recordings

This study proposed a method for detecting lameness in dairy cows based on machine vision, addressing the challenges associated with manual detection. Data from a dairy farm in Taigu, Shanxi, China were collected and divided into two parts. The first part was utilized to precisely position the cow's back by employing a dedicated deep learning model named GhostNet_YOLOv4, which can be implemented on mobile or embedded devices. The second part was used with the Visual Background Extractor (Vibe) algorithm, incorporating additional morphological processing techniques. Enhancing the Vibe algorithm, a widely used background subtraction algorithm for image sequences, achieved more accurate recognition of the specific pixel areas of cows. Subsequently, cow shape-related feature parameters were extracted from the back area using the combined approach. These parameters were used to calculate the average curvature, which describes the degree of curvature of the cow's back contour during walking. The differences in curvature values were employed for classification to detect lameness. Through extensive experimentation, distinct average curvature ranges of [−0.025, −0.125], [−0.025, +∞], and [−∞, −0.125] were established for normal cows, early lameness, and moderate-severe lameness, respectively. The algorithm's effectiveness was validated by processing 600 image sequences of dairy cows, resulting in a lameness detection accuracy of 91.67%. These findings can serve as a reference for the timely and accurate recognition of lameness in dairy cows.

Online communities can be an attractive source of ideas for product and process innovations. However, innovative user‐contributed ideas may be few. From a perspective of harnessing “big data” for inbound open innovation, the detection of good ideas in online communities is a problem of detecting rare events. Recent advances in text analytics and machine learning have made it possible to screen vast amounts of online information and automatically detect user‐contributed ideas. However, it is still uncertain whether the ideas identified by such systems will also be regarded as sufficiently novel, feasible and valuable by firms who might decide to develop them further. A validation study is reported in which 200 posts from an online home brewing community were extracted by an automatic idea detection system. Two professionals from a brewing company evaluated the posts in terms of idea content, idea novelty, idea feasibility and idea value. The results suggest that the automatic idea detection system is sufficiently valid to be deployed for the harvesting and initial screening of ideas, and that the profile of the identified ideas (in terms of novelty, feasibility and value) follows the same pattern identified in studies of user ideation in general.

This paper describes an automatic oil leak detection system which can detect accidental oil leaks at an early stage by continuously and remotely monitoring the underground tank that stores oil for standby engine generators. The engine generator is a regular power supply of an emergency backup power supply for telecommunication facilities. The system is made up of a liquid level meter, an oil leak detection sensor and measurement/monitoring equipment for measuring the amount of oil and monitoring the leak. The liquid level meter measures the amount of oil stored in the tank. Oil leaks from the tank are automatically detected by the oil leak detection sensor. The measurement/monitoring equipment displays the amount of oil on a LCD, and regularly as well as upon demand forwards this information to the remote monitoring center. Moreover, this equipment displays the warnings on both the LCD and a LED, and forwards warnings such as oil leaks to the remote monitoring center. The introduction of this system will help detect accidental oil leaks at an early stage. This system is expected to reduce or eliminate the need for maintenance checks, greatly reducing working hours and expenditures required for the maintenance of the underground tanks.

In this paper proposed a system for automatic defects detection in images. The solution to this problem is widely used in practice. Automatic detection is found in the challenge of detecting defects on the road surface, in the textile industry, as well as virtual restoration of archival photo images. The solution to this range of problems allows speeding up work in these areas, and in some cases, completely solving. To solve the first two problems (search for defects on the pavement and textiles), it is enough to create a mask that localizes defects in the image with maximum reliability, while photo restoration requires additional algorithms to restore the detected damaged areas. The proposed method is based on the latest achievements in the field of machine learning and allows solve the main disadvantages of traditional methods. Automatic defect detection is performed using a neural network with compound descriptor. A series of experiments confirmed the high efficiency of the proposed method in comparison with traditional methods for detecting defects.

The ferrite magnetic tiles are widely used in industry field. At present, the defects detection in ferrite magnetic tile surfaces is done by manual work. In order to improve the defects detection efficiency and prevent missed and false detection, an automatic detection system applied to the magnetic tiles non-destructive detection was proposed based on computer vision. A suit of the automatic defects detection equipment used for magnetic tile surfaces was designed so that we can adjust the position and angle of the lightings and cameras programmatically to meet the requirement of different location for different kinds of magnetic tiles. To solve the problem of automatically detect defects from magnetic tile images which are with dark colors and low contrasts, a new hybrid algorithm which combines nonsubsampled Contourlet transform and Laws texture feature measurement was proposed to eliminate the influence of the grinding textures and extract defects. In this methodology the original image was first decomposed by nonsubsampled Contourlet transform, the characteristics of the decomposition coefficients are analyzed by Laws texture feature measurement. Then according to the texture energies of the restructured image, a segmentation threshold was determined to reset the decomposition coefficients. Finally the image was reconstructed with the reconstruction coefficients, the grinding textures were eliminated and defects were obtained by Canny operator. The experimental results show that based on the proposed method, the grinding textures can be eliminated effectively, the defects can be extracted accurately, and the accuracy rate of extraction defects can achieve 93.57%. The automatic detection system can provide an effective solution to magnetic tile defects detection industry.

Lameness in dairy cows is a serious issue that not only affects the health and wellbeing of animals but also affects the economic status of dairy farms. Lameness is the result of stress and pain, ultimately leading to production losses. Lameness is not only the cause of the decrease in production at the dairy farm but is also a welfare issue. Lameness can be caused by many factors, most notably health issues, housing and management, nutrition, and infections. It is important to understand the causes of lameness in dairy cattle in order to prevent the lameness of animals at the farm. A good understanding of the causes can help in developing better regimes for control of this health issue, formulating housing conditions at the farm, and adapting preventive measures to prevent productive losses due to lameness. From hosing to feeding and from management to hoof, health issues should be addressed in the daily routine at a farm to prevent lameness in cows. This review article will delve deeply into the causative factors that are the source of lameness in dairy cattle, ultimately causing dairy losses at the farm.

The data of a wafer with defects can provide engineers with very important information and clues to improve the yield rate and quality in manufacturing. This paper presents a microscope automatic positioning and wafer detection system with human-machine interface based on image processing and fuzzy inference algorithms. In the proposed system, a XY table is used to move the position of each die on 6 inch or 8 inch wafers. Then, a high-resolution CCD and one set of two-axis optical linear encoder are used to accurately measure the position on the wafer. Finally, the developed human-machine interface is used to display the current position of an actual wafer in order to complete automatic positioning, and a wafer map database can be created. In the process of defect detection, CCD is used for image processing, and during preprocessing, it is required to filter noise, acquire the defect characteristics, define the defective template, and then take the characteristic points of the defective template as the reference input for fuzzy inference. A high-accuracy optical automatic positioning and wafer defect detection system is thus constructed. This study focused on automatic detection of spots, scratches, and bruises, and attempted to reduce the time to detect defective die and improve the accuracy of determining the defects of semiconductor devices.

Media bias and the intolerance of media outlets and citizens to deal with opposing points of view pose a threat to the proper functioning of democratic processes. In this respect, we present a systematic review of the literature related to media bias detection, in order to characterize and classify the different types of media bias, and to explore the state-of-the-art of automatic media bias detection systems. The main objectives of this paper were twofold. First, we framed information, misinformation and disinformation within a theoretical framework that allows us to differentiate the different existing misinformation problems such as us media bias, fake news, or propaganda. Second, we studied the state of the art of automatic media bias detection systems: analyzing the most recently used techniques and their results, listing the available resources and the most relevant datasets, and establishing a discussion about how to increase the maturity of this area. After doing a comprehensive literature review, we have identified and selected a total of 17 forms of media bias that can be classified depending on the context (e.g., coverage bias, gatekeeping bias, or statement bias), and on the author’s intention (e.g., spin bias, or ideology bias). We also reviewed, following the PRISMA methodology, the main automatic media bias detection systems that have been developed so far, selecting 63 relevant articles, from which we extracted the most used techniques; including non-deep learning methods (e.g., linguistic-based methods, and reported speech-based methods), and deep learning methods (e.g., RNNs-based methods, and transformers-based methods). Additionally, we listed and summarized 18 available datasets for the task of automatic media bias detection. In conclusion, the current methods for automatic media bias detection are still in their infancy and there is still a lot of potential for improvement in terms of accuracy and robustness. We have proposed some future research lines that could potentially contribute to the development of more advanced techniques.

PurposeIn the fabric manufacturing industry, various unfavorable factors, including machine fault and yarn breakage, can easily cause fabric defects and affect product quality, begetting huge economic losses to enterprises. Thus, automatic fabric defect detection systems have become an important development direction. Herein, the most common defects in the fabric production process, like ribbon yarn, broken yarn, cotton ball, holes, yarn shedding and stains, are detected. Current fabric defect detection systems afford low detection accuracy and a high missed detection rate for small target fabric defects. Therefore, this study proposes deep learning technology for automatically detecting fabric defects by improving the YOLOv5s target detection algorithm. The improved algorithm is termed YOLOv5s-4SCK, which can effectively detect fabric defects. This study aims to discuss the aforementioned issues.Design/methodology/approachSpecifically, based on the YOLOv5s algorithm, first, the structure of YOLOv5s is modified to add a small target detection layer, fully utilize deep and shallow features and reduce the missed detection rate of small target fabric defects. Second, the integration of CARAFE upsampling enables the effective retention of feature information and maintenance of a certain computational efficiency, thereby improving the detection accuracy. Finally, the K-Means++ clustering algorithm is used to analyze the position of the center point of the prior box to better obtain the anchor box and improve the average accuracy and evaluation index of detection.FindingsThe research results show that the YOLOv5s-4SCK algorithm increases the accuracy by 4.1% and the detection speed by 2 f.s-1 compared to the original YOLOv5s algorithm, and it effectively improves the original YOLOv5s problem of high missed detection rate of small targets.Research limitations/implicationsThe YOLOv5s-4SCK proposed in this paper can effectively reduce the missed detection rate of fabric defects, improve the detection efficiency and has certain industrial value.Practical implicationsThe proposed algorithm can quickly identify fabric defects, effectively improving the detection rate. In the future, the proposed algorithm will be applied in the actual industry.Social implicationsAutomatic fabric defect detection reduces the manpower of inspectors, and the proposed YOLOv5s-4SCK algorithm is also suitable for other recognition fields.Originality/valueThe proposed YOLOv5s-4SCK algorithm has been tested using real cloth to ensure its accuracy, and its performance is better than the original YOLOv5s algorithm.

In this paper, we present a system for automatic object detection and pose estimation from a single depth map containing multiple objects for bin-picking applications. The proposed object detection algorithm is based on matching the keypoints extracted from the depth image by using the RANSAC algorithm with the spin image descriptor. In the proposed system, we combine the keypoint detection and the RANSAC algorithm to detect the objects, followed by the ICP algorithm to refine the 3D pose estimation. In addition, we implement the proposed algorithm on the GPGPU platform to speed-up the computation. Experimental results on simulated depth data are shown to demonstrate the proposed system.

Bearded seal vocalizations were recorded by four spatially separated receivers on the Chukchi Continental Slope in Alaska in 2016–2017. Bearded seals vocalizations are often analyzed manually or by using automatic detections that are manually validated. An automatic detection and classification system (DCS) based on convolutional neural networks (CNNs) is proposed. The DCS is divided into two sections. First, regions of interest (ROI) containing potential bearded seal calls are detected through a 2D normalized cross-correlation of the measured spectrogram and a representative template of the two calls of interest considered in this work. Second, CNNs are used to classify the ROIs to determine if they are noise or a specified vocalization. The CNNs are trained on 80% of the ROIs manually labeled from one of the recorders and validated on the remaining 20% with a classification accuracy above 95.5%. When assessing the generalization performance of the DCS, we tested on the remaining three recorders located at different positions, obtaining a precision above 89.2% for the main class of the two types of calls. This study provides evidence that CNNs are suitable for classifying bearded seal vocalizations from ROIs found by classic detection techniques. [Work supported by ONR via Grant No. N00014-18-1-2140.]

Avalanche occurrences are unambiguous indicators of unstable snow conditions. Information on past and current avalanche activity is therefore crucial for avalanche forecasting. To continuously assess avalanche activity, automatic detection systems are required. In recent years, technological and signal processing advances have led to the development of operational infrasound avalanche detection systems. We evaluated the detection performance of four operationally running infrasound detection systems installed at three different sites in the Swiss Alps during two entire winter seasons. To this end, we collected a comprehensive data set of avalanche activity using a network of automatic cameras and supplementary field observations by local observers. The events automatically identified by the systems were then compared to the data set of visually observed avalanches. Only 3% of the 839 observed avalanches were associated with automatic detections and 21% of the automatic detections were confirmed by field observations. However, the majority of observed avalanches were small and most automatic detections occurred during periods of poor visibility. Furthermore, the probability of detection (POD) increased with avalanche size, decreased with distance, and varied with avalanche type. Dry-snow avalanches were generally better detected than wet-snow and mixed-type avalanches. Large avalanches (on the order of 300 m wide and 1000 m long) within a distance of 3 km from the array were typically well detected (POD ≈ 90%). The false alarm ratio was estimated to 13–30%.