Incremental learning of deep neural network for robust vehicle classification

Existing single-lane free flow (SLFF) tolling systems either heavily rely on contact-based treadle sensor to detect the number of vehicle wheels or manual operator to classify vehicles. While the former is susceptible to high maintenance cost due to wear and tear, the latter is prone to human error. This paper proposes a vision-based solution to SLFF vehicle classification by adapting a state-of-the-art object detection model as a backbone of the proposed framework and an incremental training scheme to train our VehicleDetNet in a continual manner to cater the challenging problem of continuous growing dataset in real-world environment. It involved four experiment set-ups where the first stage involved CUTe datasets. VehicleDetNet is utilized for the framework of vehicle detection, and it presents an anchorless network which enable the elimination of the bounding boxes of candidates’ anchors. The classification of vehicles is performed by detecting the vehicle's location and inferring the vehicle's class. We augment the model with a wheel detector and enumerator to add more robustness, showing improved performance. The proposed method was evaluated on live dataset collected from the Gombak toll plaza at Kuala Lumpur-Karak Expressway. The results show that within two months of observation, the mean accuracy increases from 87.3 % to 99.07 %, which shows the efficacy of our proposed method

Car detection using cascade classifier on embedded platform

Advanced Driver-Assistance Systems (ADAS) help reducing traffic accidents caused by distracted driving. One of the features of ADAS is Forward Collision Warning System (FCWS). In FCWS, car detection is a crucial step. This paper explains about car detection system using cascade classifier running on embedded platform. The embedded platform used is NXP SBC-S32V234 evaluation board with 64-bit Quad ARM Cortex-A53. The system algorithm is developed in C++ programming language and used open source computer vision library, OpenCV. For car detection process, object detection by cascade classifier method is used. We trained the cascade detector using positive and negative instances mostly from our self-collected Malaysian road dataset. The tested car detection system gives about 88.3 percent detection accuracy with images of 340 by 135 resolution (after cropped and resized). When running on the embedded platform, it managed to get average 13 frames per second with video file input and average 15 frames per second with camera input

Assessing Factors Contribute to Unclaimed Properties in Selangor--Post-pandemic Scenario

Real estate planning is the exertion plan, which must be made to distribute the property owned by the property owner to the beneficiaries when the owner passes away in order to prevent problems or arguments later on. This paper aimed to study the effect between knowledge, heir’s awareness, and complex administration process toward unclaimed properties among residents in Selangor in the context of post-pandemic scenario. This study reviewed the literature of past research of factors that influenced unclaimed properties. The construct variables used in this study were knowledge, awareness, and complex administration process. This study adopts online survey questionnaire to collect 384 valid responses from the residents in Shah Alam, Selangor. These collected data were analyzed using SPSS version 25 and the results were gathered through pearson’s correlation coefficient. The findings revealed all variables were significantly influenced by unclaimed properties. The implication of this study was to focus on smaller sample in Selangor while sample size should give another significant outcome. Government and state authority must come up with mountainous strategies to overcome this issue by imposing several initiatives. Keywords: faraid, heirs awareness, knowledge, Malaysia, unclaimed propertie

Probabilistic-Based Analysis for Damaging Features of Fatigue Strain Loadings

This paper presents the behaviour of fatigue damage extraction in fatigue strain histories of automotive components using the probabilistic approach. This is a consideration for the evaluation of fatigue damage extraction in automotive components under service loading that is vital in a reliability analysis. For the purpose of research work, two strain signals data are collected from a car coil spring during a road test. The fatigue strain signals are then extracted using the wavelet transform in order to extract the high amplitude segments that contribute to the fatigue damage. At this stage, the low amplitude segments are removed because of their minimal contribution to the fatigue damage. The fatigue damage based on all extracted segments is calculated using some significant strain-life models. Subsequently, the statistics-based Weibull distribution is applied to evaluate the fatigue damage extraction. It has been found that about 70% of the probability of failure occurs in the 1.0 x 10-5 to 1.0 x 10-4 damage range for both signals, while 90% of the probability of failure occurs in the 1.0 x 10-4 to 1.0 x 10-3 damage range. Lastly, it is suggested that the fatigue damage can be determined by the Weibull distribution analysi

Motion estimation on homogenous surface for around view monitoring system

Around View Monitoring (AVM) system uses multiple input cameras mounted on different positions of a vehicle to display 360° bird-eye-view around the vehicle that is not readily visible to the driver. The development of this system will contribute to the reduction of parking accidents by monitoring its surroundings, detecting lanes and identifying obstacles. With AVM, we can significantly decrease the number of minor accidents. AVM will not only be used for parking assistance but can also assist navigation in the narrow path area. Conventional AVM systems developed in the market using four or six cameras and requires an additional sensor for detection in order to minimise stitching error or to reduce the time to calibrate the output display image. The procedure is time-consuming and increases the cost of development. We propose to develop two ultra-wide-angle cameras located on the front and rear vehicle integrated with the motion estimation (ME) algorithm to produce a parking bird eye view and forward/backward trajectory lines. From our ablative analysis, optical flow is not suitable to be used for real-time ADAS systems as it fails at least 25.5% of the time. However, block matching algorithm based on normalized cross-correlation (CCORR NORMED) and normalised correlation coefficient (CCOEFF NORMED) were able to detect all templates correctly with 0% of false detection on our dataset

Internal works quality assessment for wall evenness using vision-based sensor on a mecanum-wheeled mobile robot

Robotics in the construction industry has been used for a few decades up to this present time. There are various advanced robotics mechanisms or technologies developed for specific construction task to assist construction. However, not many researches have been found on the quality assessment of the finished structures. This research proposes a quality assessment robot that will assist in performing the assessment of the internal works of a building by assessing a quality assessment criterion in the Malaysian Construction Industry Standards. There are various assessment criteria such as hollowness, cracks and damages, finishing and jointing. This paper will focus on the wall evenness using a camera mounted on a mobile robot with a Mecanum wheel design. The wall evenness assessment was done via projecting a laser leveler on the wall and capturing the images by using a camera, which is later processed by a central controller. Results show that the deviation calculation method can be used to differentiate between even and uneven walls. Pixel deviations for even walls show values of less than 15 while uneven walls show values of more than 20 pixels

Efficient region of interest based metric learning for effective open world deep face

Face Recognition (FR) has recently gained traction as a widely used biometric for securitybased applications such as facial recognition payment. The widespread use is due to improvements in deep convolutional neural networks (CNN) and large datasets. However, FR is still an ill-posed problem, especially in an open world scenario. Existing FR methods require finetuning, classifier retraining, or global metric learning to improve the performance for effective domain adaptation. It incurs an undesirable downtime. Open world FR must identify the persons for whom the FR model is not trained. It also produces imbalanced pairs, giving a false sense of high performance. The popular fixed threshold strategies, such as σ values, also lead to sub-optimal performance. This paper proposes a fast and efficient threshold adapter algorithm using an effective Region of Interest (ROI) setting for metric learning. It uses five different ROI schemes to find an adaptive threshold in real-time. The algorithm also determines the FR model quality and usability after new enrolments. To establish the effectiveness, we investigated various threshold finding strategies for five state-of-the-art face recognition algorithms for open world adaptation on different datasets.We also proposed a novel performance evaluation metric for FR algorithms on imbalanced datasets. Experimental results demonstrated that the proposed metric learning is up to 12 times faster than the nearest competitor while reporting higher accuracy and fewer errors. The study suggests that the F1-score is vital as a performance indicator for imbalanced pair evaluation, and accuracy at the highest reported F1-score is the desired metric for benchmarking FR algorithms in open world

Environmental impacts of utilization of ageing fixed offshore platform for ocean thermal energy conversion

Most Malaysian jacket platforms have outlived their design life. As these old platforms have outlived their design life, other alternatives must be considered. As several offshore oil and gas extraction installations approach the end of their operational life, many options such as decommissioning and the development of a new source of energy such as wind farms are introduced. The objective of this paper is to investigate the environmental impacts of utilising ageing fixed offshore platform as a source for Ocean Thermal Energy Conversion (OTEC). The environmental impact of utilising an ageing fixed offshore platform as an OTEC source is discussed. OTEC produces energy by taking advantage of temperature variations between the ocean surface water and the colder deep water through cold-water intake piping, which requires a seawater depth of 700 metres. The output of this study shows that OTEC is envisioned to preserve marine life, becoming a new and reliable source of energy, assist clean water production, and reduce the negative impact of climate change. OTEC platforms utilising ageing platforms may lead to 44 % of fish catch in the ocean, remove 13 GW of surface ocean heat for every GW of electricity production per year, generate 1.3105 tonnes of hydrogen per year for each GW of electricity generated. In addition, OTEC platforms can reduce approximately 5106 tonnes of carbon dioxide from the environment for 1 GW of electricity generated per year, and supply 2 million litres of water per day for a 1 MW platform. Since Malaysia's seawater profile allows for installing a fixed offshore platform as an OTEC power plant, Malaysia has many potentials to profit from the OTEC process

Software optimization of vision-based around view monitoring system on embedded platform

Image processing algorithm requires high computational power. Optimizing the algorithm to be run on an embedded platform is very critical as the platform provides limited computational resources. This research focused on optimizing and implementing a vision-based Around View Monitoring (AVM) system running on two embedded boards of Cortex-A7 quad and Cortex-A15 quad-core, and desktop platform of Intel i7 core. This paper presented a study on several techniques of software optimization that is removing code redundancy and multi-threading. The two methods improve the total processing time of the AVM system by 45% on ARM Cortex-A15 and 47% on ARM Cortex-A7