In day-to-day activities, the advanced technology like Internet of Things (IoT) emerges to improve the lifestyle of people. In metropolitan cities, real-time parking is the long-lasting problem that we face in our daily life activities. Urban parking regulation gained more attention because of its capability to diminish energy consumption, congested traffic, and manifestation. The parking space detection for vehicles in real-time is a crucial role for on-street parking control models where the data is delivered to the drivers through internet with the help of Global Positioning System (GPS). Hence, the network congestion gets increased, where the existing model does not performed well in the parking space effectively. This research paper recommends a novel strategy for detecting occupancy in road traffic parking by IoT and deep networks to overcome these difficulties. At first, the required information is gathered from the standard datasets as CNRPark + EXT for further processing. After the data collection process, the preprocessing phase is executed, where the data cleaning and data transformation approaches are done with a multi-scale retinex network. The pre-processing technique is effectively performed to reduce background noise. Further, the feature extraction is progressed through the Residual Attention Network, where the relevant features are extracted from the pre-processed data. Subsequently, the optimal features and weights are selected by the novel Advanced Pelican Optimization Algorithm (APOA) to get the optimal weighted feature selection. This optimal weighted feature is further given to the Ensemble Deep Networks (EDN) by integrating the Deep Conditional Random Field (DCRF), and Extreme Deep Learning (EDL) model. In final process, the detection of occupancy in road traffic parking is determined by averaging both classifiers outcomes. Finally, the results are compared with numerous optimization algorithms and classifiers to ensure the developed system's efficacy. Throughout the validation, the developed model outperforms with an accuracy as 96.6, precision as 92.9 and Mathew's Correlation Coefficient (MCC) as 92.3. Thus, the developed model shows better performance than the existing traditional approaches.

中文翻译：

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用于道路交通停车的集成深度学习智能占用检测网络的基于高级优化的加权特征


在日常活动中，物联网（IoT）等先进技术的出现改善了人们的生活方式。在大都市中，实时停车是我们日常生活中长期面临的问题。城市停车监管因其减少能源消耗、减少交通拥堵和表现的能力而受到更多关注。实时车辆停车位检测对于路边停车控制模型至关重要，其中数据借助全球定位系统（GPS）通过互联网传送给驾驶员。因此，网络拥堵加剧，现有模型在停车位上的表现不佳。本研究论文提出了一种通过物联网和深度网络检测道路交通停车占用情况的新策略，以克服这些困难。首先，从 CNRPark + EXT 等标准数据集中收集所需信息以进行进一步处理。数据收集过程之后，执行预处理阶段，其中数据清理和数据转换方法是通过多尺度 Retinex 网络完成的。有效地执行预处理技术以减少背景噪声。此外，特征提取是通过残余注意力网络进行的，其中相关特征是从预处理的数据中提取的。随后，通过新颖的高级鹈鹕优化算法（APOA）选择最佳特征和权重，以获得最佳加权特征选择。通过集成深度条件随机场（DCRF）和极限深度学习（EDL）模型，将这种最佳加权特征进一步赋予集成深度网络（EDN）。 在最终过程中，道路交通停车占用率的检测是通过对两个分类器结果进行平均来确定的。最后，将结果与众多优化算法和分类器进行比较，以确保所开发系统的有效性。在整个验证过程中，开发的模型表现出色，准确度为 96.6，精确度为 92.9，马修相关系数 (MCC) 为 92.3。因此，所开发的模型比现有的传统方法表现出更好的性能。