Effectiveness of Human Detection from Aerial Images Taken from Different Heights

Salem, Muhammad Shahir Hakimy; Zaman, Fadhlan Hafizhelmi Kamaru

doi:10.18421/tem102-06

Cited by 2 publications

(3 citation statements)

References 27 publications

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“…Yolo has capabilities in fast detection [6,15] but has shortcomings in precision. Object detection algorithms have been widely applied in various fields including vehicle detection [16,17], objects [6,18], plant diseases [13,19,20], health [21,22], housing [23], road damage [24], natural disasters [25,26] to weapons detection [27]. Currently, Yolo is the most popular object detection method due to its accuracy and speed, but one of its shortcomings is the dataset used.…”

Section: Literature Reviewmentioning

confidence: 99%

Measuring Agricultural Area Using YOLO Object Detection and ArUco Markers

Masykur,

Adi,

Nurhayati

2024

ISI

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This paper discusses the use of drones in image acquisition of agricultural land to detect the presence of disease and calculate the area of infected agriculture. Calculation of the area of infected and healthy areas will be calculated by combining the You Only Look Once (Yolo) object detection algorithm version 4 with the ArUco Marker reference image. The image resulting from the detection from the Yolo v4 algorithm will be used as a reference to be referenced using a reference image in the form of an AruCo Marker to convert it to area units to determine the area of the infected area and calculate the ratio between the area of the infected area and the area of the healthy area. The coordinate points at each corner are used as the first stage in converting pixels into area units. Measuring the infected area is necessary to localize the infection so that it does not spread to healthy plant areas. Apart from that, to anticipate the spread of infection which could result in crop failure. Evaluation of the calculation of the area of the detection area with the actual area resulted in an accuracy of 97.05%.

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Section: Literature Reviewmentioning

confidence: 99%

Measuring Agricultural Area Using YOLO Object Detection and ArUco Markers

Masykur,

Adi,

Nurhayati

2024

ISI

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“…Research performance is measured by making comparisons with previous research, namely research conducted by Salem, et al [27]. In this study using drones or Unmanned Aerial Vehicles (UAV) as equipment in aerial image acquisition.…”

Section: Comparisonmentioning

confidence: 99%

“…Figure 8. Training Model Results Resume (a) Map (b) Average LossThe performance of the training results model with a variety of images showed that there are differences in the values because image acquisition is influenced by several things, for example, acquisition distance, light intensity during image acquisition, camera specifications, acquisition time, weather conditions and labeling[27]. The final results of training the model with various image acquisition distances are shown in Table3.…”

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confidence: 99%

Approach and Analysis of Yolov4 Algorithm for Rice Diseases Detection at Different Drone Image Acquisition Distances

2023

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Rice production plays an important role in people's lives globally because rice is the most widely consumed staple food for over half of the world's human population. Unfortunately, the rice plants are prone to pests and diseases which may result in a decrease in the rice production. Thus, early and accurate detection of rice diseases is needed. This paper discusses a method for detecting rice diseases called You Only Look Once (YOLO) object detection algorithm version 4. A drone camera was used to acquire the images from four different distances, namely 2 meters, 5 meters, 10 meters and 20 meters. This approach aims to detect the presence of pests to be faster, more accurate and more precise. The precision results at each image capture from the different distances were 46.8%, 48%, 65% and 77.3% with the average loss value of 6.52, 0.54, 1.16 and 2.73

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Effectiveness of Human Detection from Aerial Images Taken from Different Heights

Cited by 2 publications

References 27 publications

Measuring Agricultural Area Using YOLO Object Detection and ArUco Markers

Measuring Agricultural Area Using YOLO Object Detection and ArUco Markers

Approach and Analysis of Yolov4 Algorithm for Rice Diseases Detection at Different Drone Image Acquisition Distances

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