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| Publication | Contribution | Comments |
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| Bin Liu et al. (2017). Study of object detection based on faster R-CNN | Implementation of the faster R-CNN algorithm to detect objects such as cats, humans, cars, and horses [21] | Related research demonstrates the classification process using faster R-CNN + PVANET, but produces a not high average precision of 84.9%. Therefore, the author does not combine several methods based on the consideration that the faster R-CNN method can still produce higher precision values |
| Shih-Chung Hsu et al. (2018). Vehicle detection using simplified fast R-CNN | Implementation of fast R-CNN algorithm to detect vehicles [22] | Related research presents a fast and straightforward method by modifying fast R-CNN to be able to detect and localize vehicles in various displays effectively. Therefore, the author uses the faster R-CNN method, which develops the fast R-CNN method, where faster R-CNN can process faster recognition |
| Beibei Zhu et al. (2016). Automatic detection of books based on faster RCNN | Implementation of the faster R-CNN algorithm to detect books based on the object’s shape [23] | In a related study, we adopted the faster R-CNN code framework, which was created to implement efficient and accurate book detection. Although in line with the author in using the faster R-CNN method, the negative object recognition test is more complex because it is carried out on various types of coloring |
| Xiaochun Mai et al. (2018). Faster R-CNN with classifier fusion for small fruit detection | Implementation of the faster R-CNN algorithm using five convolution screens to detect almonds still on the tree [24] | Related research explores the effectiveness of faster R-CNN to improve object classification, but some labeling and annotation errors result in uncertainty. Based on the constraints found in previous research, the authors consider using a larger sample training dataset to reduce the uncertainty during the object recognition process |
| Wei Zhang et al. (2018). Deconv R-CNN for small object detection on remote sensing images | Implementation of the R-CNN algorithm succeeded in detecting microscopic aircraft objects taken from a height [25] | Related research can detect small objects, aircraft, and ships with time efficiency and high-detection precision. However, the author still needs to prove the results of the unique detection of ships if they are in an ocean area with sea color conditions that can change. This follows the plastic object detection tests carried out based on various hues categories |
| Mohamed badawy (2020). Sea turtle detection using faster R-CNN for conservation purpose | The method suggested an intelligent system for sea turtles detection where the faster R-CNN algorithm is employed impressively and gives promising results [26] | The related research applies the faster R-CNN method for turtle detection through the onboard camera mounted on the drone, which effectively contributes to ecosystem solutions and environmental research in general and turtle conservation projects. Therefore, as a support, the authors conducted research on different cases but used the same method and had the same goal, namely focusing on ecosystem solutions for turtles |
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