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All Journal JOIV : International Journal on Informatics Visualization
Abdul Haris Rangkuti
Bina Nusantara University, Jakarta, Indonesia
Computer Science & IT

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Optimizing Hand Gesture Recognition Using CNN Model Supported by Raspberry pi for Self-Service Technology Abdul Haris Rangkuti; Varyl Hasbi Athalaa; Farrel Haridhi Indallah; Fajar Febriansyah Febriansyah
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1032

Abstract

This study describes the optimization of hand gesture recognition on Raspberry Pi 4 technology has advanced over the past years, some computers are now able to compute much more complex problems like real-time object detection. But for small devices, optimization is required to run in real-time with acceptable performance in terms of latency and low-cost effect on accuracy. Low latency is a requirement for most technology, especially when integrating real-time object detection as input into Self-Service Technology on Raspberry Pi for the store. This research was conducted on 288 pictures with six types of chosen hand gestures for command inputs that have been configured in the Self-Service Technology as a training dataset. In the experiment carried out with 5 CNN object detection models were used, namely YOLOv3-Tiny-PRN, YOLOv4-Tiny, MobileNetV2-Yolov3-NANO, YOLO-Fastest-1.1, and YOLO-Fastest-1.1-XL. Based on the experiment after optimization, the FPS and inference time metrics have improved performance. The performance improves due to a gained average value of FPS by 3 FPS and a reduced average value of inference time by 119,260 ms. But such an improvement also comes with a reduction in overall accuracy. The rest of the parameters have a reduced score on Precision, Recall, F1-Score, and some for IoU. Only YOLO-Fastest-1.1-XL have an improved value of IoU by about 0.58%. Some improvements in the CNN and dataset might improve the performance even more without sacrificing too much on the accuracy, but it's most likely suitable for another research as a continuation of this topic.
Optimization of Vehicle Object Detection Based on UAV Dataset: CNN Model and Darknet Algorithm Abdul Haris Rangkuti; Varyl Hasbi Athala
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1159

Abstract

This study was conducted to identify several types of vehicles taken using drone technology or Unmanned Aerial Vehicles (UAV). The introduction of vehicles from above an altitude of more than 300-400 meters that pass the highway above ground level becomes a problem that needs optimum investigation so that there are no errors in determining the type of vehicle. This study was conducted at mining sites to identify the class of vehicles that pass through the highway and how many types of vehicles pass through the road for vehicle recognition using a deep learning algorithm using several CNN models such as Yolo V4, Yolo V3, Densenet 201, CsResNext –Panet 50 and supported by the Darknet algorithm to support the training process. In this study, several experiments were carried out with other CNN models, but with peripherals and hardware devices, only 4 CNN models resulted in optimal accuracy. Based on the experimental results, the CSResNext-Panet 50 model has the highest accuracy and can detect 100% of the captured UAV video data, including the number of detected vehicle volumes, then Densenet and Yolo V4, which can detect up to 98% - 99%. This research needs to continue to be developed by knowing all classes affordable by UAV technology but must be supported by hardware and peripheral technology to support the training process.
A Novel Approach of Animal Skin Classification Using CNN Model with CLAHE and SUCK Method Support Abdul Haris Rangkuti; Varyl Athala Hasbi
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1153

Abstract

This study describes the process of classifying animal skin images which are rather difficult to obtain optimal image characteristics. For this reason, in the pre-processing stage, we propose two methods to support feature extraction: sharpening using a convolutional kernel (SUCK-Sharpening) and adaptive histogram equalization with limited contrast (CLAHE-Equalized). SUCK works by operating on these pixel values using direct math to build a new image; this final value is the new value of the current pixel. CLAHE overcomes the limitations of the global approach by performing local contrast enhancement. Because of the advantages of the two methods, it becomes a solution to get features processed at the feature extraction and classification stage. The process of animal skin imagery has characteristics in terms of shape and texture, including the characteristics of animal skin color. In this study, some experiments have been carried out on several CNN models, with an average classification accuracy of more than 70% using the sharpened and equalized methods on six animal skins. More detail, the average classification accuracy using 3 CNN models supported by two methods, namely Sharpening and Equalize on the CNN Resnet 50V2 model is 67.73% and 73.78%, InceptionV3 model at 82.13%, and 74.76% and Densenet121 models were 87.64% and 87.46 %. This research can be continued to improve the accuracy of other animal skin images, including determining fake or genuine skin images.
Co-Authors Fajar Febriansyah Febriansyah Farrel Haridhi Indallah Varyl Athala Hasbi Varyl Hasbi Athala Varyl Hasbi Athalaa