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""This book brings together various research methodologies and trends in emerging areas of application of computer vision and image processing for those interested in the research developments of this rapidly growing field"--"

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Pengembangan algoritma untuk kendali quadrotor semakin masif dilakukan oleh peneliti diseluruh dunia. Sama seperti manusia yang melihat dan kemudian dapat mendekati dan menyentuh suatu obyek, penelitian ini juga diarahkan untuk menciptakan prinsip yang sama yang kami sebut sebagai Image Loop Control (ILC). Proses pendeteksian objek memanfaatkan kecerdasan buatan YOLOv8 (AI deep learning) sebagai state-of-the-art pada dunia pendeteksian objek kecil membawa performa pendeteksian objek kecil ke tingkat yang lebih tinggi dengan inovasinya yang revolusioner. Penerapannya di quadrotor diharapkan dapat memungkinkan tingkat otonomi pada otomasi quadrotor melalui image loop control tersebut. Di dalam ILC tetap digunakan kendali Proporsional dan Differensial (PD) untuk mengendalikan gerak pada tiap sumbu gerakan. Skripsi ini melaporkan gerak yaw yang dilakukan oleh quadrotor sebagai respon dari deteksi obyek oleh YOLOv8. Pada proses validasi hasil pelatihan dataset, sebesar 96% gambar pintu tertutup terdeteksi sebagai close, 94% gambar pintu terbuka terdeteksi sebagai open, dan 87% gambar pintu setengah terbuka terdeteksi sebagai semi. Hasil proses image loop control respon kontroler PD di sumbu yaw, memiliki rata-rata time delay sebesar 0,98 detik, rata-rata rise time sebesar 1,26 detik, dan rata-rata settling time sebesar 8,62 detik menggunakan nilai Kp = 1,2 dan Kd = 0,5.

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The development of quadrotor control algorithm has been extensively pursued by numerous researchers around the world. Similar to how humans can look, move around, and interact with an object, this research aims to achieve the same through a principle we define as the Image Loop Control (ILC). The process of object detection using the artificial intelligence YOLOv8 (deep learning AI) as the state-of-the-art in the small object detection world has brought the performance of small object detection algorithms to a higher level thanks to its revolutionary innovation. Its implementation in a quadrotor may enhance the degree of autonomy on automated quadrotors by using an image loop control. Within the ILC framework, we use a Proportional and Differential (PD) controller to control quadrotor movements along each axis. This thesis presents the performance of yawing movements executed by the quadrotor in response to object detections identified by the YOLOv8. During the validation process of the trained dataset, the system detected 96% of closed doors accurately, 94% of open doors accurately, and 87% of semi opened doors accurately. The response of the image loop control response using a PD controller on the yaw axis resulted in an average time delay of 0.98 seconds, average rise time of 1.26 seconds, and average settling time of 8.62 seconds with the values Kp = 1.2 and Kd = 0.5."

"ABSTRAKProses pemantauan tool wear pada micromilling membutuhkan ketelitian yang tinggi, proses ini dapat dilakukan menggunakan mikroskop digital Dino-Lite dan mikroskop elektron. Namun penggunaan mikroskop elektron membutuhkan waktu yang lama karena harus datang ke laboratorium mikroskop elektron. Maka dari itu dibuatlah perancangan teknologi computer vision berbasis image processing untuk mendeteksi luas wear pada tool micromilling. Proses pengembangan menggunakan fitur OpenCV program Python. Proses dimulai dengan mengambil gambar tool baru dan tool rusak menggunakan Dino-Lite dengan spesifikasi gambar yang sama persis. Kemudian gambar diimpor ke program Python dan dikonversi menjadi bentuk HSV (Hue, Saturation, Value). Gambar HSV kemudian diberikan fitur noise reduction menggunakan gaussian blur untuk mengurangi noise pada gambar. Gambar HSV yang sudah diberikan fitur noise reduction kemudian diberi fitur color detection untuk mendapatkan thresholding dari hasil pengaturan variabel masking HSV. Hasil thresholding kemudian diberikan fitur image Canny sebagai fitur pendeteksian luas berdasarkan kontur gambar hasil thresholding. Kemudian nilai luas permukaan tool baru dan tool rusak akan muncul. Kedua nilai ini akan dibandingkan dan menghasilkan persentase tool wear. Pengujian yang penulis lakukan adalah dengan membuat variasi variabel noise reduction menggunakan gaussian blur, nilai gaussian blur yang diberikan sebesar 0, 1, 3, 5, 7, 9, 11, 13, 15 dan 17 (nilai gaussian blur hanya bisa 0 dan bilangan ganjil). Data yang diperoleh ada yang tidak lengkap karena keadaan gambar yang tidak mendukung, namun dengan keberadaan gaussian blur, dapat membantu perekaman luas. Hasil menunjukkan bahwa semakin tinggi nilai gaussian blur, maka meningkatkan potensi gambar kontur tool dapat dideteksi luasnya.

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ABSTRACT

Tool wear monitoring on micromilling needs a high value of accuracy, this process can be done using Dino-Lite digital microscope and electron microscope. However, the usage of electron microscope needs a long period of time since we have to go to the electron microscope laboratory. Therefore, the design of image processing-based computer vision for tool wear monitoring on micromilling was developed. The development process uses OpenCV feature on Python. The process begins with gathering the images of the new tool and the broken tool using Dino-Lite with exactly the same image properties. The images are then imported to Python and converted to HSV format (Hue, Saturation, Value). The HSV images are then given a noise reduction feature using Gaussian Blur to reduce the noise of the images. The HSV images that have been given the noise reduction feature are then given a color detection feature to obtain thresholding of the results of the HSV masking variable adjustment. The thresholding results are the given an image Canny feature as the contour area detection from the thresholding results. Afterwards, the face area value of the new tool and the broken tool will be displayed. These two values will be compared and generate the tool wear percentage. The experiment that the authors has done is to make variations in noise reduction variables using gaussian blur, the given gaussian blur values are 0, 1, 3, 5, 7, 9, 11, 13, 15 dan 17 (only 0 and odd numbers of the gaussian blur value can only be added). The data that be obtained are not complete due to the unsupported image condition, however, in the presence of the gaussian blur, could support the documentation process. At the end, the results show that the tool area on the images are more potential to be detected due to the increasing number of gaussian blur value."

"The seven-volume set comprising LNCS volumes 7572-7578 constitutes the refereed proceedings of the 12th European Conference on Computer Vision, ECCV 2012, held in Florence, Italy, in October 2012. The 408 revised papers presented were carefully reviewed and selected from 1437 submissions. The papers are organized in topical sections on geometry, 2D and 3D shape, 3D reconstruction, visual recognition and classification, visual features and image matching, visual monitoring: action and activities, models, optimisation, learning, visual tracking and image registration, photometry: lighting and colour, and image segmentation."

"The seven-volume set comprising LNCS volumes 7572-7578 constitutes the refereed proceedings of the 12th European Conference on Computer Vision, ECCV 2012, held in Florence, Italy, in October 2012. The 408 revised papers presented were carefully reviewed and selected from 1437 submissions. The papers are organized in topical sections on geometry, 2D and 3D shapes, 3D reconstruction, visual recognition and classification, visual features and image matching, visual monitoring: action and activities, models, optimisation, learning, visual tracking and image registration, photometry: lighting and colour, and image segmentation."

"The seven-volume set comprising LNCS volumes 7572-7578 constitutes the refereed proceedings of the 12th European Conference on Computer Vision, ECCV 2012, held in Florence, Italy, in October 2012. The 408 revised papers presented were carefully reviewed and selected from 1437 submissions. The papers are organized in topical sections on geometry, 2D and 3D shapes, 3D reconstruction, visual recognition and classification, visual features and image matching, visual monitoring: action and activities, models, optimisation, learning, visual tracking and image registration, photometry: lighting and colour, and image segmentation."

"The seven-volume set comprising LNCS volumes 7572-7578 constitutes the refereed proceedings of the 12th European Conference on Computer Vision, ECCV 2012, held in Florence, Italy, in October 2012. The 408 revised papers presented were carefully reviewed and selected from 1437 submissions. The papers are organized in topical sections on geometry, 2D and 3D shape, 3D reconstruction, visual recognition and classification, visual features and image matching, visual monitoring: action and activities, models, optimisation, learning, visual tracking and image registration, photometry: lighting and colour, and image segmentation."

"The seven-volume set comprising LNCS volumes 7572-7578 constitutes the refereed proceedings of the 12th European Conference on Computer Vision, ECCV 2012, held in Florence, Italy, in October 2012. The 408 revised papers presented were carefully reviewed and selected from 1437 submissions. The papers are organized in topical sections on geometry, 2D and 3D shapes, 3D reconstruction, visual recognition and classification, visual features and image matching, visual monitoring: action and activities, models, optimisation, learning, visual tracking and image registration, photometry: lighting and colour, and image segmentation."