PERBANDINGAN KINERJA METODE YOLO V7, SSD, RETINANET, DAN SCALED YOLO V4 UNTUK DETEKSI OBJEK KERUSAKAN PADA PERMUKAAN JALAN

Jalan merupakan salah satu aspek penting yang sangat dibutuhkan dalam kemajuan suatu daerah di berbagai aspek baik dari segi ekonomi, sosial, dan politik. Jalan yang terus menerus dilalui oleh volume lalu lintas kendaraan yang tinggi dapat menyebabkan kerusakan pada permukaan jalan tersebut. Pengidentifikasian jenis kerusakan jalan secara otomatis merupakan hal yang perlu dilakukan dalam upaya penanganan kerusakan jalan. Dari permasalahan tersebut, penelitian ini bertujuan untuk membangun model pendeteksi kerusakan pada permukaan jalan menggunakan metode You Only Look Once (YOLO) v7. Selain itu, pada penelitian ini juga diimplementasikan beberapa metode objek deteksi lainnya yaitu Single Shot Detector (SSD), RetinaNet dan Scaled YOLOv4 untuk memperoleh gambaran perbandingan kinerja YOLOv7 dengan model SSD, RetinaNet dan Scaled YOLOv4. Hasil penelitian menunjukkan bahwa model YOLOv7 merupakan model deteksi terbaik dibandingkan model lainnya dengan nilai [email protected] sebesar 70.8% dengan kecepatan deteksi sebesar 23.07 millisecond per gambar. Model Scaled YOLOv4 mendapatkan nilai [email protected] sebesar 60.2% dengan kecepatan deteksi sebesar 38.43 millisecond per gambar. Model RetinaNet mendapatkan nilai [email protected] sebesar 56.8% dengan kecepatan deteksi sebesar 99.48 millisecond per gambar dan model SSD mendapatkan nilai [email protected] sebesar 41.2% dengan kecepatan deteksi sebesar 23.8 millisecond per gambar; Road is one of the most important aspects that is needed in the development of an area in various aspects both in terms of economic, social and political. Roads that are continuously traversed by high volumes of vehicle traffic can cause damage to the road surface. Identifying the type of road damage automatically is something that needs to be done in an effort to deal with road damage. From these problems, this study aims to build a damage detection model on the road surface using the You Only Look Once (YOLO) v7 method. In addition, this study also implemented several other object detection methods, namely Single Shot Detector (SSD), RetinaNet and Scaled YOLOv4 to obtain a comparison of the performance of YOLOv7 with SSD, RetinaNet and Scaled YOLOv4 models. The results showed that the YOLOv7 model is the best detection model compared to other models with [email protected] value of 70.8% with prediction speed of 23.07 milliseconds per image. The Scaled YOLOv4 model gets [email protected] value of 60.2% with a prediction speed of 38.43 milliseconds per image. The RetinaNet model gets [email protected] value of 56.8% with a prediction speed of 99.48 milliseconds per image and the SSD model gets [email protected] value of 41.2% with a prediction speed of 23.8 milliseconds per image

Road Damage Detection Acquisition System based on Deep Neural Networks for Physical Asset Management

Research on damage detection of road surfaces has been an active area of re-search, but most studies have focused so far on the detection of the presence of damages. However, in real-world scenarios, road managers need to clearly understand the type of damage and its extent in order to take effective action in advance or to allocate the necessary resources. Moreover, currently there are few uniform and openly available road damage datasets, leading to a lack of a common benchmark for road damage detection. Such dataset could be used in a great variety of applications; herein, it is intended to serve as the acquisition component of a physical asset management tool which can aid governments agencies for planning purposes, or by infrastructure mainte-nance companies. In this paper, we make two contributions to address these issues. First, we present a large-scale road damage dataset, which includes a more balanced and representative set of damages. This dataset is composed of 18,034 road damage images captured with a smartphone, with 45,435 in-stances road surface damages. Second, we trained different types of object detection methods, both traditional (an LBP-cascaded classifier) and deep learning-based, specifically, MobileNet and RetinaNet, which are amenable for embedded and mobile and implementations with an acceptable perfor-mance for many applications. We compare the accuracy and inference time of all these models with others in the state of the art

Iteratively Optimized Patch Label Inference Network for Automatic Pavement Disease Detection

We present a novel deep learning framework named the Iteratively Optimized Patch Label Inference Network (IOPLIN) for automatically detecting various pavement diseases that are not solely limited to specific ones, such as cracks and potholes. IOPLIN can be iteratively trained with only the image label via the Expectation-Maximization Inspired Patch Label Distillation (EMIPLD) strategy, and accomplish this task well by inferring the labels of patches from the pavement images. IOPLIN enjoys many desirable properties over the state-of-the-art single branch CNN models such as GoogLeNet and EfficientNet. It is able to handle images in different resolutions, and sufficiently utilize image information particularly for the high-resolution ones, since IOPLIN extracts the visual features from unrevised image patches instead of the resized entire image. Moreover, it can roughly localize the pavement distress without using any prior localization information in the training phase. In order to better evaluate the effectiveness of our method in practice, we construct a large-scale Bituminous Pavement Disease Detection dataset named CQU-BPDD consisting of 60,059 high-resolution pavement images, which are acquired from different areas at different times. Extensive results on this dataset demonstrate the superiority of IOPLIN over the state-of-the-art image classification approaches in automatic pavement disease detection. The source codes of IOPLIN are released on \url{https://github.com/DearCaat/ioplin}.Comment: Revision on IEEE Trans on IT

Road Pavement Damage Detection using Computer Vision Techniques: Approaches, Challenges and Opportunities

The work presented in this paper is the result of a preliminary research aimed at using computer vision techniques for road pavement damage detection in the context of a smart city. It first introduces the related concepts. Then, it surveys the state of the art and existing solutions, presenting their main features, strengths and limitations. The most promising solutions are identified. Finally, it discusses open challenges and research directions in this area

Object Detection in 20 Years: A Survey

Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Its development in the past two decades can be regarded as an epitome of computer vision history. If we think of today's object detection as a technical aesthetics under the power of deep learning, then turning back the clock 20 years we would witness the wisdom of cold weapon era. This paper extensively reviews 400+ papers of object detection in the light of its technical evolution, spanning over a quarter-century's time (from the 1990s to 2019). A number of topics have been covered in this paper, including the milestone detectors in history, detection datasets, metrics, fundamental building blocks of the detection system, speed up techniques, and the recent state of the art detection methods. This paper also reviews some important detection applications, such as pedestrian detection, face detection, text detection, etc, and makes an in-deep analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible publicatio

Road pavement damage detection using computer vision techniques: Approaches, challenges and opportunities

Este artigo apresenta uma visão geral e os resultados de uma investigação preliminar que visa a utilização de técnicas de visão computacional para deteção de defeitos em pavimentos rodoviários, no contexto de uma cidade inteligente. Primeiro introduz os conceitos relacionados. Em seguida, faz um levantamento do estado da arte e das soluções existentes, apresentando as suas principais características, pontos fortes e limitações. São identificadas as soluções mais promissoras. Para finalizar discute desafios em aberto e direções de investigação nesta área.info:eu-repo/semantics/publishedVersio

Deep Learning Approaches in Pavement Distress Identification: A Review

This paper presents a comprehensive review of recent advancements in image processing and deep learning techniques for pavement distress detection and classification, a critical aspect in modern pavement management systems. The conventional manual inspection process conducted by human experts is gradually being superseded by automated solutions, leveraging machine learning and deep learning algorithms to enhance efficiency and accuracy. The ability of these algorithms to discern patterns and make predictions based on extensive datasets has revolutionized the domain of pavement distress identification. The paper investigates the integration of unmanned aerial vehicles (UAVs) for data collection, offering unique advantages such as aerial perspectives and efficient coverage of large areas. By capturing high-resolution images, UAVs provide valuable data that can be processed using deep learning algorithms to detect and classify various pavement distresses effectively. While the primary focus is on 2D image processing, the paper also acknowledges the challenges associated with 3D images, such as sensor limitations and computational requirements. Understanding these challenges is crucial for further advancements in the field. The findings of this review significantly contribute to the evolution of pavement distress detection, fostering the development of efficient pavement management systems. As automated approaches continue to mature, the implementation of deep learning techniques holds great promise in ensuring safer and more durable road infrastructure for the benefit of society

Hardware faults that matter: Understanding and Estimating the safety impact of hardware faults on object detection DNNs

Object detection neural network models need to perform reliably in highly dynamic and safety-critical environments like automated driving or robotics. Therefore, it is paramount to verify the robustness of the detection under unexpected hardware faults like soft errors that can impact a systems perception module. Standard metrics based on average precision produce model vulnerability estimates at the object level rather than at an image level. As we show in this paper, this does not provide an intuitive or representative indicator of the safety-related impact of silent data corruption caused by bit flips in the underlying memory but can lead to an over- or underestimation of typical fault-induced hazards. With an eye towards safety-related real-time applications, we propose a new metric IVMOD (Image-wise Vulnerability Metric for Object Detection) to quantify vulnerability based on an incorrect image-wise object detection due to false positive (FPs) or false negative (FNs) objects, combined with a severity analysis. The evaluation of several representative object detection models shows that even a single bit flip can lead to a severe silent data corruption event with potentially critical safety implications, with e.g., up to (much greater than) 100 FPs generated, or up to approx. 90% of true positives (TPs) are lost in an image. Furthermore, with a single stuck-at-1 fault, an entire sequence of images can be affected, causing temporally persistent ghost detections that can be mistaken for actual objects (covering up to approx. 83% of the image). Furthermore, actual objects in the scene are continuously missed (up to approx. 64% of TPs are lost). Our work establishes a detailed understanding of the safety-related vulnerability of such critical workloads against hardware faults.Comment: 15 pages, accepted in safecomp22 conferenc