Thorough and precise estrus detection plays a crucial role in the fertility of dairy cows. Farmers commonly used direct visual monitoring in recognizing estrus signs which demands time and effort and causes misinterpr...
详细信息
Thorough and precise estrus detection plays a crucial role in the fertility of dairy cows. Farmers commonly used direct visual monitoring in recognizing estrus signs which demands time and effort and causes misinterpretations. The primary sign of estrus is the standing heat, where the dairy cows stand to be mounted by other cows for a few seconds. Through the years, researchers developed various detection methods, yet most of these methods involve contact and invasive approaches that affect the estrus behaviors of cows. So, the proponents developed a non-invasive and non-contact estrus detection system using image processing to detect standing heat behaviors. Through the tensorflow object detection api, the proponents trained two custom neural network models capable of visualizing bounding boxes of the predicted cow objects on image frames. The proponents also developed an object overlapping algorithm that utilizes the bounding box corners to detect estrus activities. Based on the conducted tests, an estrus event occurs when the centroids of the detected objects measure a distance of less than 360px and have two interior angles with another fixed point of less than 25 degrees and greater than 65 degrees for Y and X axes, respectively. If the conditions are met, the program will save the image frame and will declare an estrus activity. Otherwise, it will restart its estrus detection and counting. The system observed 17 cows, a carabao, and a bull through the cameras installed atop of a cowshed, and detects the estrus events with an efficiency of 50%.
India is the second populated country across the globe, with a rough population of 1.35 billion. Around 5.6 million tonnes of plastic wastes are generated every year, which is approximately about 15,342 tonnes per day...
详细信息
ISBN:
(纸本)9783030372187;9783030372170
India is the second populated country across the globe, with a rough population of 1.35 billion. Around 5.6 million tonnes of plastic wastes are generated every year, which is approximately about 15,342 tonnes per day [1]. India produces more plastic than its recycling limit. Multi-National companies like Frito Lay, PepsiCo are accountable for most of the waste generated. The Government of India has decided to make India plastic-free by 2022, though the rules and regulations pertaining to reach the goal are not robust enough to inflict any change but the citizens can make this change. We will explore a potential solution using Machine Learning, Image processing and objectdetection to assist the smooth profiling of the waste in our locality.
Real time objectdetection is one of the challenging task as it need faster computation power in identifying the object at that time. However the data generated by any real time system are unlabelled data which often ...
详细信息
ISBN:
(纸本)9781538694718
Real time objectdetection is one of the challenging task as it need faster computation power in identifying the object at that time. However the data generated by any real time system are unlabelled data which often need large set of labeled data for effective training purpose. This paper proposed a faster detection method for real time objectdetection based on convolution neural network model called as Single Shot Multi-Box detection(SSD).This work eliminates the feature resampling stage and combined all calculated results as a single component. Still there is a need of a light weight network model for the places which lacks in computational power like mobile devices(eg: laptop, mobile phones, etc). Thus a light weight network model which use depth-wise separable convolution called MobileNet is used in this proposed work. Experimental result reveal that use of MobileNet along with SSD model increase the accuracy level in identifying the real time household objects.
This master thesis describes a practical implementation of a deep learning frame- work for objectdetection on the self-collected multiclass dataset. The research work presents multiple perspectives of the data collec...
详细信息
This master thesis describes a practical implementation of a deep learning frame- work for objectdetection on the self-collected multiclass dataset. The research work presents multiple perspectives of the data collection, labelling, preprocessing and training popular objectdetection architectures. The challenges in the collection of multiclass objectdetection dataset from the indoor premises and annotation process are presented with possible solutions. The performance evaluations of the trained object detectors are measured in terms of precision, recall, F 1 -score, mAP and pro- cessing speed. We experimented multiple objectdetection architectures that were available on the tensorflowobjectdetection model zoo. The multiclass dataset collected from the indoor premises were used to train and evaluate the performance of modern convolutional objectdetection models. We studied two scenarios, (a) pretrained objectdetection model and (b) fine-tuned detection model on the self-collected mul- ticlass dataset. The performance of fine-tuned object detectors was better than the pretrained detectors. From our experiment, we found that region based convolu- tional neural network architectures have superior detection accuracy on our dataset. Faster region-based convolutional neural network (RCNN) architecture with residual networks features extractor has the best detection accuracy. Single shot multi-box detector (SSD) models are comparatively less precise in detection. However, they are faster in computation and easier to deploy in mobile and embedded devices. It is found that the region-based fully convolutional network (RFCN) is the suitable al- ternative for multi-class objectdetection considering the speed/accuracy trade-offs.
Tato diplomová práce se zabývá detekcí prvků uživatelského rozhraní na obrázku displejetiskárny za použití konvolučních neuronových sítí. V teoreti...
详细信息
Tato diplomová práce se zabývá detekcí prvků uživatelského rozhraní na obrázku displejetiskárny za použití konvolučních neuronových sítí. V teoretické části je provedena rešeršesoučasně používaných architektur pro detekci objektů. V praktické čísti je probrána tvorbagalerie, učení a vyhodnocování vybraných modelů za použití tensorflowobjectdetectionapi. Závěr práce pojednává o vhodnosti vycvičených modelů pro zadaný úkol.
暂无评论