Comparison of Faster R-CNN and YOLOv5 for Overlapping Objects Recognition

يعد تصنيف الجسم المتداخل أحد التحديات الرئيسية التي يواجهها الباحثون الذين يعملون في اكتشاف الأشياء والتعرف عليها. معظم الخوارزميات المتاحة التي تم تطويرها قادرة فقط على تصنيف أو التعرف على الأشياء التي تكون إما منفصلة بشكل فردي عن بعضها البعض أوجسم  واحد في مشهد (مشاهد) ، ولكن لا تتداخل مع اجسام  أدوات المطبخ. في هذا المشروع ، تم اقتراح خوارزميات Faster R-CNN و YOLOv5 لاكتشاف وتصنيف جسم متداخل في منطقة المطبخ. تم تطبيق YOLOv5 و Faster R-CNN على االاجسام المتداخلة حيث من المتوقع أن يتمكن المرشح أو النواة من فصل االجسم المتداخل في الطبقة المخصصة لتطبيق النماذج. تم استخدام قاعدة بيانات الصور المعيارية لأدوات المطبخ وأدوات المطبخ المتداخلة من الإنترنتااجسام مرجعية أساسية. تم تعيين مجموعات التقييم والتدريب / التحقق عند 20٪ و 80٪ على التوالي. قام هذا المشروع بتقييم أداء هذه التقنيات وتحليل نقاط قوتها وسرعاتها بناءً على الدقة والدقة ودرجةF1.  خلصت نتائج التحليل في هذا المشروع إلى أن YOLOv5 ينتج مربعات إحاطة دقيقة بينما يكتشف Faster R-CNN المزيد من االاجسام. في بيئة اختبار مماثلة ، يُظهر YOLOv5 أداءً أفضل من خوارزمية R-CNN الأسرع. بعد التشغيل في نفس البيئة، حصل هذا المشروع على دقة 0.8912 (89.12٪) لـ YOLOv5 و 0.8392 (83.92٪) لـ Faster R-CNN ، بينما كانت قيمة الخسارة 0.1852 لـ YOLOv5 و 0.2166 لأسرع  R-CNN. تعد المقارنة بين هاتين الطريقتين هي الأكثر حداثة ولم يتم تطبيقها مطلقًا في الكائنات المتداخلة وخاصة أدوات المطبخ.Classifying an overlapping object is one of the main challenges faced by researchers who work in object detection and recognition. Most of the available algorithms that have been developed are only able to classify or recognize objects which are either individually separated from each other or a single object in a scene(s), but not overlapping kitchen utensil objects. In this project, Faster R-CNN and YOLOv5 algorithms were proposed to detect and classify an overlapping object in a kitchen area.  The YOLOv5 and Faster R-CNN were applied to overlapping objects where the filter or kernel that are expected to be able to separate the overlapping object in the dedicated layer of applying models. A kitchen utensil benchmark image database and overlapping kitchen utensils from internet were used as base benchmark objects. The evaluation and training/validation sets are set at 20% and 80% respectively. This project evaluated the performance of these techniques and analyzed their strengths and speeds based on accuracy, precision and F1 score. The analysis results in this project concluded that the YOLOv5 produces accurate bounding boxes whereas the Faster R-CNN detects more objects. In an identical testing environment, YOLOv5 shows the better performance than Faster R-CNN algorithm. After running in the same environment, this project gained the accuracy of 0.8912(89.12%) for YOLOv5 and 0.8392 (83.92%) for Faster R-CNN, while the loss value was 0.1852 for YOLOv5 and 0.2166 for Faster R-CNN. The comparison of these two methods is most current and never been applied in overlapping objects, especially kitchen utensils

Machine learning with multistage classifiers for identification of of ectoparasite infected mud crab genus Scylla

Recently, the mud-crab farming can help the rural population economically. However, the existing parasite in the mud-crabs could interfere the long live of the mud-crabs. Unfortunately, the parasite has been identified to live in hundreds of mud-crabs, particularly it happened in Terengganu Coastal Water, Malaysia. This study investigates the initial identification of the parasite features based on their classes by using machine learning techniques. In this case, we employed five classifiers i.e logistic regression (LR), k-nearest neighbors (kNN), Gaussian Naive Bayes (GNB), support vector machine (SVM), and linear discriminant analysis (LDA). We compared these five classfiers to best performance of classification of the parasites. The classification process involving three stages. First, classify the parasites into two classes (normal and abnormal) regardless of their ventral types. Second, classified sexuality (female or male) and maturity (mature or immature). Finally, we compared the five classifiers to identify the species of the parasite. The experimental results showed that GNB and LDA are the most effective classifiers for carrying out the initial classification of the rhizocephalan parasite within the mud crab genus Scylla

Optimization of Image Features using Artificial Bee Colony Algorithm and Multi-layered Perceptron Neural Network for Texture Classification

One of the fundamental issues in texture classification is the suitable selection combination of input parameters for the classifier. Most researchers used trial and observation approach in selecting the suitable combination of input parameters. Thus it leads to tedious and time consuming experimentation. This paper presents an automated method for the selection of a suitable combination of input parameters for gray level texture image classification. The Artificial Bee Colony (ABC) algorithm is used to automatically select a suitable combination of angle and distance value setting in the Gray Level Co-occurrence (GLCM) matrix feature extraction method. With this setting, 13 Haralick texture features were fed into Multi-layer Perceptron Neural Network classifier. To test the performance of the proposed method, a University of Maryland, College Park texture image database (UMD Database) is employed. The texture classification results show that the proposed method could provide an automated approach for finding the best input parameters combination setting for GLCM which leads to the best classification accuracy performance of binary texture image classification

Classification of Coral Reef Components Using Color and Texture Features

This paper presents classification of coral reef benthic components that composed of live corals, dead corals, rubbles and sands. Since coral reef exist with different of shapes, colours and textures, the use of image processing technique provides advantages to estimate percentage cover of coral reef benthic components. Color and texture are used to extract features of coral reef benthic components. Hue Saturation Value (HSV) color model is utilized by calculating its color histogram to obtain color features. Meanwhile, the Local Binary Pattern (LBP) descriptor is used to extract texture features. The color and texture features are combined as the input into the Multilayer Perceptron Neural Network (MLPNN) classifier. The performances of the coral reef classification are evaluated based on color feature, texture feature or combination of both color and texture features. It is found out that the joining feature set of color and texture features provide the highest classification accuracy, i.e. 92.60% accuracy rate as compared to the use of individual feature such as color and texture features alone that achieved only 81.30% and 88.10% accuracy classification rate, respectively

Modified JSEG algorithm for reducing over-segmentation problems in underwater coral reef images

The original JSEG algorithm has proved to be very useful and robust in variety of image segmentation case studies.However, when it is applied into the underwater coral reef images, the original JSEG algorithm produces over-segementation problem, thus making this algorithm futile in such a situation. In this paper, an approach to reduce the over-segmentation problem occurred in the underwater coral reef image segmentation is presented. The approach works by replacing the color histogram computation in region merge stage of the original JSEG algorithm with the new computation of color and texture features in the similarity measurement. Based on the perceptual observation results of the test images, the proposed modified JSEG algorithm could automatically segment the regions better than the original JSEG algorithm

Monitoring climate change effects on coral reefs using edge-based image segmentation

Coral reefs are valuable ecosystems that face vulnerability to climate change impacts. Underwater images often encounter noise from various factors, such as water turbidity, lighting conditions, attenuation, and scattering, which can complicate edge detection and segmentation processes, leading to inaccuracies. However, image processing techniques offer a viable solution to this issue. In this study, an edge-based segmentation approach is proposed that uses multiple contrast techniques to detect and quantify changes in coral reef imagery. The proposed approach effectively identifies changes in coral reef imagery, making it a valuable tool for monitoring climate change's effects on these ecosystems. Furthermore, high-resolution images at different time points and locations were collected, and then an edge-based segmentation approach was utilized to enhance the accuracy of edge detection and segmentation. Comparing the proposed method with traditional segmentation techniques showed a significant improvement in terms of segmentation precision. Subsequently, alterations in the structure and composition of coral reefs are observed, indicating the influence of climate change on these ecosystems. This research highlights the capabilities of image processing techniques using edge-based segmentation in monitoring coral reefs. It offers an effective and precise approach to detecting changes in coral reef images, thereby contributing to conservation endeavors

Optimization of discrete wavelet transform features using artificial bee colony algorithm for texture image classification

Selection of appropriate image texture properties is one of the major issues in texture classification. This paper presents an optimization technique for automatic selection of multi-scale discrete wavelet transform features using artificial bee colony algorithm for robust texture classification performance. In this paper, an artificial bee colony algorithm has been used to find the best combination of wavelet filters with the correct number of decomposition level in the discrete wavelet transform.  The multi-layered perceptron neural network is employed as an image texture classifier.  The proposed method tested on a high-resolution database of UMD texture. The texture classification results show that the proposed method could provide an automated approach for finding the best input parameters combination setting for discrete wavelet transform features that lead to the best classification accuracy performance

Improved Coral Reef Images Segmentation using Modified JSEG Algorithm

Underwater coral reef image segmentation suffers from various challenges due to various factors especially variation in illumination, different water turbidity, different water depth, variation in color, texture and shape of the coral reef species. In this paper, we modified an original automatic color image segmentation called JSEG to enable better coral reef segmentation process. The modification involves the substitution of General Lloyd Algorithm and agglomerative algorithm in the original JSEG version with the k-means algorithm. In addition, the newly modified JSEG algorithm process image in L*a*b color space to provide better processing of underwater image color property while k-means algorithm is used to segment the color within the specified cluster number. The experimental results showed that the modified JSEG algorithm could segment the coral reefs better than the original JSEG algorithm