Robust object detection in the wild via cascaded DCGAN

This research deals with the challenges of object detection at a distance or low resolution in the wild. The main intention of this research is to exploit and cascade state-of-the-art models and propose a new framework for enabling successful deployment for diverse applications. Specifically, the proposed deep learning framework uses state-of-the-art deep networks, such as Deep Convolutional Generative Adversarial Network (DCGAN) and Single Shot Detector (SSD). It combines the above two deep learning models to generate a new framework, namely DCGAN-SSD. The proposed model can deal with object detection and recognition in the wild with various image resolutions and scaling differences. To deal with multiple object detection tasks, the training of this network model in this research has been conducted using different cross-domain datasets for various applications. The efficiency of the proposed model can further be determined by the validation of diverse applications such as visual surveillance in the wild in intelligent cities, underwater object detection for crewless underwater vehicles, and on-street in-vehicle object detection for driverless vehicle technologies. The results produced by DCGAN-SSD indicate that the proposed method in this research, along with Particle Swarm Optimization (PSO), outperforms every other application concerning object detection and demonstrates its great superiority in improving object detection performance in diverse testing cases. The DCGAN-SSD model is equipped with PSO, which helps select the hyperparameter for the object detector. Most object detectors struggle in this regard, as they require manual effort in selecting the hyperparameters to obtain better object detection. This research encountered the problem of hyperparameter selection through the integration of PSO with SSD. The main reason the research conducted with deep learning models was the traditional machine learning models lag in accuracy and performance. The advantage of this research and it is achieved with the integration of DCGAN-SSD has been accommodated under a single pipeline

Deep learning based pedestrian detection at distance in smart cities

Generative adversarial networks (GANs) have been promising for many computer vision problems due to their powerful capabilities to enhance the data for training and test. In this paper, we leveraged GANs and proposed a new architecture with a cascaded Single Shot Detector (SSD) for pedestrian detection at distance, which is yet a challenge due to the varied sizes of pedestrians in videos at distance. To overcome the low-resolution issues in pedestrian detection at distance, DCGAN is employed to improve the resolution first to reconstruct more discriminative features for a SSD to detect objects in images or videos. A crucial advantage of our method is that it learns a multi-scale metric to distinguish multiple objects at different distances under one image, while DCGAN serves as an encoder-decoder platform to generate parts of an image that contain better discriminative information. To measure the effectiveness of our proposed method, experiments were carried out on the Canadian Institute for Advanced Research (CIFAR) dataset, and it was demonstrated that the proposed new architecture achieved a much better detection rate, particularly on vehicles and pedestrians at distance, making it highly suitable for smart cities applications that need to discover key objects or pedestrians at distance