Technology Reports of Kansai University (ISSN: 04532198) is a monthly peer-reviewed and open-access international Journal. It was first built in 1959 and officially in 1975 till now by kansai university, japan. The journal covers all sort of engineering topic, mathematics and physics. Technology Reports of Kansai University (TRKU) was closed access journal until 2017. After that TRKU became open access journal. TRKU is a scopus indexed journal and directly run by faculty of engineering, kansai university.
Technology Reports of Kansai University (ISSN: 04532198) is a peer-reviewed journal. The journal covers all sort of engineering topic as well as mathematics and physics. the journal's scopes are
in the following fields but not limited to:
Detection of objects that are not common is an important issue in the field of classification of vehicles to come. In object detection, many objects have been classified, such as cars, motorbikes, bicycles, and others. But there are still many objects that are not common, especially in Indonesia. There are still many objects that only exist in Indonesia but not in other countries. Like illegal parking, carts street vendors, and many more. Therefore the writer takes the object detection theme. This proposal proposes detecting unusual objects. The object that the author will detect is illegal parking. With the tools the authors will make, there will be a new classification of illegal parking. In this way a new classification of object detection will be born, namely illegal parking objects. So foreign people will not be confused by the situation they will encounter in Indonesia. The output of this research is the label of illegal parking classification and the probability value of the results of the illegal parking classification. The object detection system uses a faster R-CNN algorithm that can work well when detecting object distances and get accurate results for distances of 6 meters by 70%, 12 meters at 90%, 18 meters at 70%
Slope Detection with road lane detection using Image Processing is one of the methods used to detect boundaries of the road using a different range of color from the road environment to distinguish road lane and non-road lane. This research using the road lane detection method that is inspired and based on previous research that we modified and develop to detect the slope of the road using OpenCV libraries and Linear Regression Method. Detection is done by making an intersection line from upper and down line that detected from Image Processing. This Research using a truck prototype with a 1:11 dimension to simulate the system and using prototype road to with the same dimension of the truck. The result shows that system can detect the slope of the road with accuracy reaching 90%
