Automated object detection of mechanical fasteners using faster region based convolutional neural networks

Mechanical fasteners are widely used in manufacturing of hardware and mechanical components such as automobiles, turbine & power generation and industries. Object detection method play a vital role to make a smart system for the society. Internet of things (IoT) leads to automation based on sensors and actuators not enough to build the systems due to limitations of sensors. Computer vision is the one which makes IoT too much smarter using deep learning techniques. Object detection is used to detect, recognize and localize the object in an image or a real time video. In industry revolution, robot arm is used to fit the fasteners to the automobile components. This system will helps the robot to detect the object of fasteners such as screw and nails accordingly to fit to the vehicle moved in the assembly line. Faster R-CNN deep learning algorithm is used to train the custom dataset and object detection is used to detect the fasteners. Region based convolutional neural networks (Faster R-CNN) uses a region proposed network (RPN) network to train the model efficiently and also with the help of Region of Interest able to localize the screw and nails objects with a mean average precision of 0.72 percent leads to accuracy of 95 percent object detectio

EYE ASPECT RATIO ADJUSTMENT DETECTION FOR STRONG BLINKING SLEEPINESS BASED ON FACIAL LANDMARKS WITH EYE-BLINK DATASET

Blink detection is an important technique in a variety of settings, including facial motion analysis and signal processing.  However, automatic blink detection is challenging due to its blink rate. This paper proposes a real-time method for detecting eye blinks in a video series. The method is based on automatic facial landmark detection trained on real-world datasets and demonstrates robustness against various environmental factors, including lighting conditions, facial emotions, and head position. The proposed algorithm calculates the position of facial landmarks, extracts scalar values using the Eye Aspect Ratio (EAR), and characterises eye proximity in each frame. For each video frame, the proposed method calculates the location of the facial landmark and extracts the vertical distance between the eyelids using the position of the facial landmark. Blinks are detected by using the EAR threshold value and recognising the pattern of EAR values in a short temporal window. According to the results from a common data set, it is shown that the proposed approach is more efficient than state-of-the-art techniques

EYE ASPECT RATIO ADJUSTMENT DETECTION FOR STRONG BLINKING SLEEPINESS BASED ON FACIAL LANDMARKS WITH EYE-BLINK DATASET

Blink detection is an important technique in a variety of settings, including facial motion analysis and signal processing.  However, automatic blink detection is challenging due to its blink rate. This paper proposes a real-time method for detecting eye blinks in a video series. The method is based on automatic facial landmark detection trained on real-world datasets and demonstrates robustness against various environmental factors, including lighting conditions, facial emotions, and head position. The proposed algorithm calculates the position of facial landmarks, extracts scalar values using the Eye Aspect Ratio (EAR), and characterises eye proximity in each frame. For each video frame, the proposed method calculates the location of the facial landmark and extracts the vertical distance between the eyelids using the position of the facial landmark. Blinks are detected by using the EAR threshold value and recognising the pattern of EAR values in a short temporal window. According to the results from a common data set, it is shown that the proposed approach is more efficient than state-of-the-art techniques.</span

Detección de colisiones mediante procesado de vídeo

Este Trabajo Fin de Grado (TFG) consta de dos partes bastante diferenciadas. En la primera, se presenta una tecnología con tanto potencial como es la detección de accidentes mediante análisis de vídeo. Se comentan las posibles aplicaciones donde su uso sería de gran utilidad y el papel que podría jugar en un futuro cercano. A continuación, se introducen los conceptos básicos en los que se basa esta ciencia, como son el Aprendizaje Automático, la Visión Artificial, las redes neuronales artificiales o la detección de objetos. De esta forma, aunque el lector no sea un experto en la materia, será capaz de hacerse una idea del contexto en el que se engloba la detección de accidentes y podrá comprender el contenido de los siguientes capítulos. La segunda parte es algo más técnica y nos centramos en un caso real de detección de accidentes que suceden alrededor de un vehículo. Basándonos en un modelo que ya había sido desarrollado y que utiliza el detector de objetos Faster R-CNN y una red neuronal VGG, realizamos un experimento modificando los módulos del detector de objetos (con EfficientDet) y de la extracción de features (con VGG16, AlexNet, DenseNet, ResNeXt). El algoritmo se entrena y testea con un dataset de clips en los que ocurren accidentes alrededor de un vehículo y es capaz de generar la probabilidad de accidente que existe para cada uno de los frames. A partir de esta probabilidad y tras un complicado procesamiento se obtienen los resultados de Area Under the Curve y el Tiempo al Accidente que permiten medir el rendimiento del experimento. Por último, se comparan y analizan todos los resultados obtenidos para encontrar la mejor estructura del modelo dependiendo de la tarea a realizar