This study presents an interesting approach to identifying the Genus of the Stingless bee aided by machine learning technology. The conventional way of identifying the Genus of the Stingless bee or “Lebah Kelulut” relied on the face-to-face meetings with local bee experts. This particular process is considered to be outdated and time consuming. Thus, the proposed solution incorporated the machine learning tool called the “TensorFlow Object Detection API”. This tool is provided by Google TensorFlow and uses the Faster Region-based Convolutional Neural Network (Faster R-CNN), which incorporates the Region Proposal Network to enhance the current network. The data set used for training and testing consisted of 400 images, which belong to two types of bee species namely, the Heterotrigona Erythrogasta and Heterotrigona Itama. The evaluation of the model produced an accuracy rate of 73.75% for an average computing time per image of 0.65 seconds.
Road signs play an essential role in ensuring road safety. However, due to external factors such as bad weather, road signs may not be in their best condition that may hinder effective communication between the road and the road users. Although the study on detection and classification of road signs is becoming more common these days, there is yet to exist a study that analyzes whether a road sign is in a good or bad condition. The basis of this study is to implement image template algorithms on road signs that will be used to analyze their physical condition as good or damaged. In addition to template matching, this study proposes a template difference method for its condition analysis. Analyses on three shapes of road signs: circular, triangular, and diamond give out the average correctly classified detection as 60% and 78%, respectively for template matching and template difference method.
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