•A lightweight CNN approach is proposed to make road surface crack detection more efficient and effective.•With the proposed CNN method, road surface crack detection can be done with mobile ...systems.•The proposed method in experimental tests was more successful than the methods in the literature.
In recent years, the worldwide road network has grown rapidly and reached an enormous size. Parallel to this size of the road network, the imperfections on the road surfaces are just as large. This has led to a significant increase in road maintenance costs for traffic management departments. Researchers have proposed methods for automatic flaw detection using deep neural networks to reduce road maintenance costs. However, these methods are insufficient in terms of performance and lightness. In this study, a convolutional neural network-based approach is proposed, which provides more successful road surface crack detection and requires less lightweight compared to other studies. Since the performance of the proposed approach is higher and more lightweight, it can be used more effectively and efficiently in mobile systems.
While automobile transportation is increasing worldwide, it also negatively affects the safety of road users. Along with the neglect of traffic rules, pedestrians account for 22% of all highway ...traffic deaths. Millions of pedestrians suffer non-fatal injuries from these accidents. Most of these injuries and deaths occur at crosswalks, where the highway and pedestrians intersect. In this study, deep learning-based a new hybrid mobile CNN approaches are proposed to reduce injuries and deaths by automatically recognizing of crosswalks in autonomous vehicles. The first of these proposed approaches is the HMCNet approach, which is a hybrid model in which the MobileNetv3 and MNasNet CNN models are used together. This model achieves approximately 2% more accuracy than the peak performance of the lean used MobileNetv3 and MNasNet models. Another proposed approach is the FHMCNet approach, which increases the success of the HMCNet approach. In the FHMCNet approach, LSVC feature selection method and SVM classification method are used in addition to HMCNet. This approach increased the classification success of HMCNet by more than approximately 2%. Finally, the proposed FHMCNet offered approximately 3% more classification accuracy than state-of-the-art methods in the literature.
Statistics on occupational accidents published by international organizations show that mobile cranes are the riskiest heavy equipment among all crane types. The most significant reason for these ...crane accidents is the decision-makers' wrong selection of mobile cranes. Making misevaluations and selecting improper mobile cranes can cause many problems besides mobile crane accidents. In addition, selecting a mobile crane is a highly complicated decision-making problem, as there are many conflicting criteria and uncertainties. Besides, no commonly accepted criteria set is used to solve this decision-making problem in the literature. The current paper proposes the fuzzy BWM (Best and Worst Method) and fuzzy MARCOS (Measurement of Alternatives and Ranking according to COmpromise Solution) combination as a robust and practical decision-making tool that can overcome many complicated ambiguities. The suggested model has been applied to solve a mobile crane selection problem encountered by a large-scale project logistics and heavy transport company in Turkey. The acquired outcomes demonstrated that technical criteria are more influential than economic and quality criteria. The criterion “Maximum hoist height” in the technical criteria group is the most influential factor in selecting the best mobile crane option. In addition, the A1 Brand-L. 4.2 is the most appropriate alternative to meet the company's requirements. Finally, a comprehensive sensitivity analysis performed to test the validity and applicability of the model approves the robustness of the model.
•The current paper presents the fuzzy BWM and MARCOS approach to mobile crane selection.•The proposed model is applied to solve mobile crane selection problems, which are highly complex and include uncertainties.•It has identified the set of selection criteria for mobile cranes for the first time.•It presents a flexible decision-making approach, which is proper for real decision-making problems.•It demonstrates interrelations among factors for making it more understandable in complex situations.
Semantic segmentation is the most important stage of making sense of the visual traffic scene for autonomous driving. In recent years, convolutional neural networks (CNN)-based methods for semantic ...segmentation of urban traffic scenes are among the trending studies. However, the methods developed in the studies carried out so far are insufficient in terms of accuracy performance criteria. In this study, a new CNN-based semantic segmentation method with higher accuracy performance is proposed. A new module, the Attentional Atrous Feature Pooling (AAFP) Module, has been developed for the proposed method. This module is located between the encoder and decoder in the general network structure and aims to obtain multi-scale information and add attentional features to large and small objects. As a result of experimental tests with the CamVid data set, an accuracy value of approximately 2% higher was achieved with a mIoU value of 70.59% compared to other state-of-art methods. Therefore, the proposed method can semantically segment objects in the urban traffic scene better than other methods.
Natural disasters are sudden and unexpected events that occur as a result of natural processes and often have a significant impact on people, animals, and plants, often resulting in material and ...emotional losses. Two of the most devastating disasters are floods and tsunamis. After these disasters, search and rescue operations to identify flooded areas are very important to save the lives of affected people and to ensure the health and safety of rescue workers. In this study, a novel semantic segmentation model called Flood Area Segmentation Network (FASegNet) was proposed to guide search and rescue teams in disaster areas to speed up search and rescue operations after natural disasters such as floods, high tides, and tsunamis. The obtained results were compared with common image segmentation models used in various fields. It was found that the developed model achieved higher accuracy rates with fewer parameters. When tested with the "Flood Area" and "Water Body" datasets without pretraining and data augmentation, FASegNet achieved mIoU accuracy of 84.3% and 84.5% respectively.
Endangered Languages of the Caucasus and Beyond offers the readers a diversity of articles by 19 prominent scholars who address the problems related to the endangered languages in Anatolia, Ukraine, ...China, Mongolia, Daghestan, Ossetia and in the Greater Himalayan region.
Karayolu yüzeyindeki asfalt çatlakları en yaygın kaplama hasarlarından biridir. Bu çatlaklar zamanında tespit edilip bakıma alınmazsa büyüyerek trafik güvenliğini ve yoğunluğunu arttıracak boyutlara ...ulaşmaktadır. Bu nedenle, asfalt çatlaklarının tespitinin hızlı bir şekilde gerçekleştirilmesi çok önemlidir. Geleneksel manuel çatlak tespiti, son derece zaman alıcı, çok maliyetli ve çok çaba gerektirir. Bu nedenle, araştırmacılar odaklarını otomatik asfalt çatlaklarının tespitine yoğunlaştırdılar. Ancak, son yıllarda otomatik çatlak tespiti kapsamlı olarak araştırılmasına rağmen çatlakların farklı yoğunluğa sahip olması ve kaplama ortamının karmaşıklığı nedeniyle hala zor bir görevdir. Bu çalışmada, bu zorluğun üstesinden gelmek için konvolüsyon sinir ağı temelli bir yöntem önerildi. Önerilen yöntem, sınıflandırma ve segmentasyondaki başarısı ve hafifliği bilinen MobileNetv2 ’nin temelde kullandığı konvolüsyon ve ters artıklı blok yapılarını baz alarak geliştirildi. Yapılan deneysel testler sonucunda, önerilen yöntemin performansı literatürdeki diğer yöntemlere göre daha yüksek olduğu görülmektedir. Bu da, otomatik asfalt çatlak tespitinin daha başarılı olması anlamına gelmektedir.
Asphalt cracks on the highway surface are one of the most common pavement damage. If these cracks are not detected and taken care of on time, they grow and reach dimensions that will increase traffic safety and density. Therefore, it is very important to carry out the detection of asphalt cracks quickly. Traditional manual crack detection is extremely time consuming, very costly and requires a lot of effort. Therefore, the researchers concentrated their focus on the detection of automatic asphalt cracks. However, although automatic crack detection has been extensively investigated in recent years, it is still a difficult task due to the fact that cracks have different densities and the complexity of the pavement environment. In this study, a convolutional neural network-based method is proposed to overcome this difficulty. The proposed method was developed based on the convolution and inverted residual block structures used by MobileNetv2, which is known for its success and lightweight in classification and segmentation. As a result of the experimental tests, it is seen that the performance of the proposed method is higher than the other methods in the literature. This means that automatic asphalt crack detection is more successful.
Günümüzde, trafik hayatında seyreden araç yoğunluğu ciddi boyutlara ulaşmıştır. Bu nedenle, mevcut ulaşım ağlarının kullanım kapasitesi maksimum seviyelere çıkmakta ve trafik sıkışıklığına yol ...açmaktadır. Akıllı Ulaşım Sistemlerinin bir çözümü olan Görsel Trafik Gözetleme Sistemleri trafik sıkışıklığını azaltmak için kullanılan alternatif yöntemlerden biridir. Görsel Trafik Gözetleme Sisteminin temel görevlerinden biri; video veya görüntülerden algılanan araç türlerini doğru bir şekilde sınıflandırmaktır. Bu çalışma, Görsel Trafik Gözetleme Sisteminin araç türlerini sınıflandırma doğruluğunu arttıracak yeni yöntemler sunmayı amaçlamaktadır. Çoğu görüntü sınıflandırma doğruluğunu arttıran çalışmalarda geleneksel yöntemler kullanılırken bu çalışmada günümüzde trend olan mobil evrişimli sinir ağları (MCNN) iki farklı yaklaşımla ele alınmaktadır. İlk olarak, MobileNetv1 ve MobileNetv2 modelleri optimize edilerek İMobileNetv1 ve İMobileNetv2 yaklaşımları önerildi. İkinci olarak, bu önerilen MCNN yaklaşımları sadece özellik çıkarıcı olarak kullanıldığı ve elde edilen özelliklerin birleştirilmesi, seçilmesi ve sınıflandırılması gibi yöntemlerin birlikte kullanıldığı bir yaklaşım önerildi. Önerilen yaklaşımlarla yapılan sınıflandırma sonucunda, %85,05 oranında çok yüksek bir sınıflandırma başarısı elde edilmiştir.
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