Realtime Object Detection via Deep Learning-based Pipelines

Ever wonder how the Tesla Autopilot system works (or why it fails)? In this tutorial we will look under the hood of self-driving cars and of other applications of computer vision and review state-of-the-art tech pipelines for object detection such as two-stage approaches (e.g., Faster R-CNN) or single-stage approaches (e.g., YOLO/SSD). This is accomplished via a series of Jupyter Notebooks that use Python, OpenCV, Keras, and Tensorflow. No prior knowledge of computer vision is assumed (although it will be help!). To this end we begin this tutorial with a review of computer vision and traditional approaches to object detection such as Histogram of oriented gradients (HOG)

Klasifikasi Citra Buah Menggunakan Convolutional Neural Network

Abstrak— Deep Learning merupakan sebuah pengembangan dari teknologi Machine Learning yang menggunakan algoritma yang dibuat berdasarkan pada hukum matematik yang bekerja layaknya otak manusia. Salah satu pemanfaatan dari deep learning adalah dalam bidang image processing atau pengolahan citra digital. Image Processing dimanfaatkan untuk membantu manusia dalam mengenali dan/atau mengklasifikasi objek dengan cepat, tepat, dan dapat melakukan proses dengan banyak data secara bersamaan. Salah Satu algoritma dari Deep learning yang digunakan dalam image processing adalah Convolutional Neural Network (CNN). Algoritma CNN terdiri dari 3 layer utama yaitu Convolutional Layer, Pooling Layer, dan Fully Connected Layer. Pada penelitian ini menggunakan arsitektur CNN dengan perpaduan 3 Convolutional Neural Network dan 2 Fully Connected Layer. Pada tahap pembuatan system klasifikasi yang menggunakan deep learning terdapat beberapa tahapan proses utama yaitu pengumpulan data, perancangan system, training, dan testing. Dataset yang diolah adalah dataset citra buah-buahan yang berasal dari dataset Fruit-360. Kelas data yang digunakan yaitu sejumlah 15 kelas dari 111 kelas pada dataset fruit-360.  Hasil dari proses learning didapatkan model CNN dengan akurasi 100% dan loss sebesar 0,012. Pada proses pengujian model CNN yang mengguakan 45 sampel citra buah didapatkan akurasi sebesar 91,42%. Sehingga dapat disimpulkan bahwa metode CNN yang dirancang pada penelitian ini dapat mengklasifikasi citra dengan baik. Kata Kunci— Deep Learning, Image Processing, Convolutional Neural Network, Fruit-360

Inter Patient Atrial Fibrillation Classification Using One Dimensional Convolution Neural Network

Atrial fibrillation is the most common type of arrhythmia. The process of detecting AF disease is quite difficult. This is because it is necessary to detect the presence or absence of a P signal wave in the ECG signal. However, this method requires special expertise from a cardiologist. Several literatures have proposed an automatic ECG classification system. However, the intra-patient paradigm does not simulate real-world scenarios. One of the challenges in the inter-patient paradigm is the morphological differences between one subject and another. In order to overcome the problems that arise in the automatic classification of ECG signal patterns a deep learning approach was proposed. This study proposes the classification process of atrial fibrillation in the inter-patient paradigm using a one-dimensional convolutional neural network architecture. The test is divided into two cases: two labels (Normal and AF) and three labels (Normal, AF and Non-AF). In the case of two test labels with an inter-patient scheme, the performance was 100% for all test metrics (accuracy, sensitivity, precision, and F1-Score). However, in the three-label case, the model's performance decreased to 85.95, 70.02, 72.50, 71.19 for accuracy, sensitivity, precision and F1-Score, respectively

Detection of Motorcycle Tire Endurance based on Tire Load Index using CNN

With increasingly rapid technological developments, the production of motorized vehicles will increase with the use of robotic power in production. The increasing number of motorized vehicles in big cities does not escape the rise of traffic accidents that occur. One aspect of accidents that we usually underestimate is the resistance of our vehicle tires to support the load on the vehicle. Therefore, we need a system to detect the resistance of a tire in supporting the load on the vehicle. For this reason, this study was conducted to detect the durability of motorcycle tires based on tire load index using a convolutional neural network. A 70% result was found in classifying tire resistance based on tire load index

Fast Crack Detection Using Convolutional Neural Network

To improve the efficiency and reduce the labour cost of the renovation process, this study presents a lightweight Convolutional Neural Network (CNN)-based architecture to extract crack-like features, such as cracks and joints. Moreover, Transfer Learning (TF) method was used to save training time while offering comparable prediction results. For three different objectives: 1) Detection of the concrete cracks; 2) Detection of natural stone cracks; 3) Differentiation between joints and cracks in natural stone; We built a natural stone dataset with joints and cracks information as complementary for the concrete benchmark dataset. As the results show, our model is demonstrated as an effective tool for industry use

Convolutional neural network in the classification of COVID-19

Covid-19 spread out rapidly around the world, forcing many countries to full shutdown, and economical and social consequences. Resulting in rapid need for new and effective methods to deal with this crisis and control it. X-ray lung images is considered one of the most effective and safe method for diagnosing Covid-19, since it could provide solid proof of the existing of the disease, and it has limited effect on the health of the human comparing with other radiography methods. In this proposed work, CNN model is designed and trained to classify Covid-19 X-ray images, by using the COVID-19 Radiography Database, which is published and available online. This database is collected by researchers and experts from various universities around the world. The database contains total of 15153 lung x-ray images, divided into three classes. The classification classes are: Normal, Covid-19, and Viral Pneumonia. The model is trained and tested on publicly available dataset. The dataset is divided into three parts: training, validation, and testing datasets. The model is evaluated based on the three of these datasets. Totally, the evaluation metrics include Accuracy, F1-score, Area Under Curve (AUC), Precision, and Recall, with values of greater than 98% for all of the evaluation metrics. Comparing the results with state of arts publications, which used the same dataset, the proposed method outperformed the state of arts publications depending on the evaluation metrics. The number of the trainable parameters in the proposed CNN model is about 25.4 millions

Penerapan CNN dengan Filter Gabor sebagai feature extractor untuk Content-Based Image Retrieval

Abstrak— Seiring dengan perkembangan teknologi informasi, kebutuhan dalam pencarian informasi menjadi hal yang penting. Jika pencarian informasi selama ini dilakukan pada data berjenis teks, maka pada perkembangan teknologi saat ini, memungkinkan adanya pencarian informasi dalam bentuk citra digital. Hal tersebut terjadi karena adanya peningkatan jumlah pustaka digital dalam bentuk citra. Sebuah metode pengembalian citra menjadi komponen utama untuk memecahkan masalah tersebut. CBIR merupakan sistem pengembalian citra yang akan membantu dalam proses pencarian citra dengan memanfaatkan fitur-fiturnya. Penggunaan ekstraksif fitur yang tepat diperlukan untuk mendapatkan fitur tersebut. Pemilihan ekstraksi ftur akan sangat memengaruhi hasil dari CBIR. Salah satu metode yang dapat melakukan ekstraksi fitur pada citra adalah CNN. Metode yang masih dalam satu jenis dalam deep learning ini mampu mempelajari fitur citra untuk dimanfaatkan ke dala bidang visi komputer. Karena itu, CNN menjadi perhatian menarik dalam penelitian ini untuk melakukan CBIR. Penggunaan filter Gabor yang mampu mendapatkan tekstur citra dengan baik juga akan diimplementasikan sebagai filter pada lapisan konvolusi CNN. Dengan menggunakan CNN dan filter gabor, penelitian ini mampu mendapatkan nilai mAP sebesar 0,895 terhadap data uji dengan dataset GHIM10k. Penelitian ini juga membandingkan beberapa metode pengukuran jarak untuk mendapatkan sistem CBIR terbaik. Kata Kunci— Content Based Image Retrieval; Convolutional Neural Networks; pengukuran jarak; filter Gabor; visi komputer