Face Mask Recognition System with YOLOV5 Based on Image Recognition

Due to carbon deposits, lean flames, or damaged metal parts, sparks can occur in aero engine chambers. At present, the detection of such sparks deeply depends on laborious manual work. Considering that interference has the same features as sparks, almost all existing object detectors cannot replace humans in carrying out high-precision spark detection. In this paper, we propose a scene-aware spark detection network, consisting of an information fusion-based cascading video codec-image object detector structure, which we name SAVSDN. Unlike video object detectors utilizing candidate boxes from adjacent frames to assist in the current prediction, we find that efforts should be made to extract the spatio-temporal features of adjacent frames to reduce over-detection. Visualization experiments show that SAVSDN can learn the difference in spatio-temporal features between sparks and interference. To solve the problem of a lack of aero engine anomalous spark data, we introduce a method to generate simulated spark images based on the Gaussian function. In addition, we publish the first simulated aero engine spark data set, which we name SAES. In our experiments, SAVSDN far outperformed state-of-the-art detection models for spark detection in terms of five metrics.

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“…The kernel size of ConvLSTM was 3. We chose YOLOv5 [ 42 ] as the object detector. SAVSDN was based on end-to-end deep learning.…”

Section: Methodsmentioning

confidence: 99%

SAVSDN: A Scene-Aware Video Spark Detection Network for Aero Engine Intelligent Test

Kou

Zhang

Huang

et al. 2021

Sensors

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“…In this paper, we use the YOLOv5 model to develop a status recognition model because YOLOv5 has been proven to perform better in detection speed compared to R-CNN families [42,43]. We aim to implement status recognition in real-time task re-assignment and task execution in a future study.…”

Section: Implementation Of Yolov5 and Transfer Learningmentioning

confidence: 99%

Status Recognition Using Pre-Trained YOLOv5 for Sustainable Human-Robot Collaboration (HRC) System in Mold Assembly

Liau

Ryu

2021

Sustainability

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Molds are still assembled manually because of frequent demand changes and the requirement for comprehensive knowledge related to their high flexibility and adaptability in operation. We propose the application of human-robot collaboration (HRC) systems to improve manual mold assembly. In the existing HRC systems, humans control the execution of robot tasks, and this causes delays in the operation. Therefore, we propose a status recognition system to enable the early execution of robot tasks without human control during the HRC mold assembly operation. First, we decompose the mold assembly operation into task and sub-tasks, and define the actions representing the status of sub-tasks. Second, we develop status recognition based on parts, tools, and actions using a pre-trained YOLOv5 model, a one-stage object detection model. We compared four YOLOv5 models with and without a freezing backbone. The YOLOv5l model without a freezing backbone gave the optimal performance with a mean average precision (mAP) value of 84.8% and an inference time of 0.271 s. Given the success of the status recognition, we simulated the mold assembly operations in the HRC environment and reduced the assembly time by 7.84%. This study improves the sustainability of the mold assembly from the point of view of human safety, with reductions in human workload and assembly time.

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“…Yolo is a single stage detection technique without a distinct region proposal and treats the detection of the target as a single regression problem [35]. Object detection using Yolov5 has been demonstrated as a superior way in comparison to other target detection and recognition algorithms [36,37,38,39]. Yolov5 has the advantages of rapid processing time in the deep learning network; ability to handle larger datasets and real-time continuous detection [40,41].…”

Section: Chicktrack Detector Model and Architecturementioning

confidence: 99%

ChickTrack - A Quantitative Tracking Tool for Measuring Chicken Activity

Neethirajan¹

2021

Preprint

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<p>The automatic detection, counting and tracking of individual and flocked chickens in the poultry industry is of paramount to enhance farming productivity and animal welfare. Due to methodological difficulties, such as the complex background of images, varying lighting conditions, and occlusions from e.g., feeding stations, water nipple stations and barriers in the chicken rearing production floor, it is a challenging task to automatically recognize and track birds using computer software. Here, a deep learning model based on You Only Look Once (Yolov5) is proposed for detecting domesticated chickens from videos with varying complex backgrounds. A multiscale feature is being adapted to the Yolov5 network for mapping modules in the counting and tracking of the trajectories of the chickens. The Yolov5 network was trained and tested on our dataset which resulted in an enhanced tracking precision accuracy. Using Kalman Filter, the proposed model was able to track multiple chickens simultaneously with the focus to associate individual chickens across the frames of the video for real time and online applications. By being able to detect the chickens amid diverse background interference and counting them precisely along with tracking the movement and measuring their travelled path and direction, the proposed model provides excellent performance for on-farm applications. Artificial intelligence enabled automatic measurements of chicken behavior on-farm using cameras offers continuous monitoring of the chicken's ability to perch, walk, interact with other birds and the farm environment, as well as the assessment of dustbathing, thigmotaxis, and foraging frequency, which are important indicators for their ability to express natural behaviors. This study highlights the potential of automated monitoring of poultry through the usage of ChickTrack model as a digital tool in enabling science-based animal husbandry practices and thereby promote positive welfare for chickens in animal farming. </p>

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Face Mask Recognition System with YOLOV5 Based on Image Recognition

Cited by 174 publications

References 16 publications

SAVSDN: A Scene-Aware Video Spark Detection Network for Aero Engine Intelligent Test

SAVSDN: A Scene-Aware Video Spark Detection Network for Aero Engine Intelligent Test

Status Recognition Using Pre-Trained YOLOv5 for Sustainable Human-Robot Collaboration (HRC) System in Mold Assembly

ChickTrack - A Quantitative Tracking Tool for Measuring Chicken Activity

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