SACM - United States of America
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9668
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Item Restricted Seeing in the Dark: Towards Robust Pedestrian Detection at Nighttime(Saudi Digital Library, 2023-12-24) Althoupety, Afnan; Feng, Wu-chi“At some point in the day, everyone is a pedestrian” a message from the National Highway Traffic Safety Administration (NHTSA) about pedestrian safety. In 2020, NHTSA reported that 6,516 pedestrians were killed in traffic crashes and a pedestrian was killed every 81 minutes on average in the United States. In relation to light condition, 77% of pedestrian fatalities occurred in the dark, 20% in daylight, 2% in dusk, and 2% in dawn. To tackle the issue from a technological perspective, this dissertation addresses the problem of pedestrian detection robustness in dark conditions, benefiting from image processing and learning-based approaches by: (i) proposing a pedestrian- luminance-aware brightening framework that moderately corrects image luminance so that pedestrians can be more robustly detected, (ii) proposing an image-to-image translation framework that learns the mapping between night and day domains through the game training of generators and discriminators and thus alleviates detecting dark pedestrian using the synthetic night images, and (iii) proposing a multi-modal framework that pairs RGB and infrared images to reduce the light factor and make pedestrian detection a fair game regardless the illumination variance.11 0Item Restricted Deep Learning for Detecting and Classifying The Growth Stages of Weeds on Fields(ProQuest, 2023) Almalky, Abeer M; Ahmed, Khaled RDue to the current and anticipated massive increase of world population, expanding the agriculture cycle is necessary for accommodating the expected human’s demand. However, weeds invasion, which is a detrimental factor for agricultural production and quality, is a challenge for such agricultural expansion. Therefore, controlling weeds on fields by accurate, automatic, low-cost, environment-friendly, and real-time weeds detection technique is required. Additionally, automating the process of detecting, classifying, and counting of weeds per their growth stages is vital for using appropriate weeds controlling techniques. The literature review shows that there is a gap in the research efforts that handle the automation of weeds’ growth stages classification using DL models. Accordingly, in this thesis, a dataset of four weed (Consolida Regalis) growth stages was collected using unnamed aerial vehicle. In addition, we developed and trained one-stage and two-stages deep learning models: YOLOv5, RetinaNet (with Resnet-101-FPN, Resnet-50-FPN backbones), and Faster R-CNN (with Resnet-101-DC5, Resnet-101-FPN, Resnet-50-FPN backbones) respectively. Comparing the results of all trained models, we concluded that, in one hand, the Yolov5-small model succeeds in detecting weeds and classifying the weed’s growth stages in the shortest inference time in real-time with the highest recall of 0.794 and succeeds in counting the instances of weeds per the four growth stages in real-time with counting time of 0.033 millisecond per frame. On the other hand, RetinaNet with ResNet-101-FPN backbone shows accurate and precise results in the testing phase (average precision of 87.457). Even though the Yolov5-large model showed the highest precision value in classifying almost all weed’s growth stages in training phase, Yolov5-large could not detect all objects in tested images. As a whole, RetinaNet with ResNet-101-FPN backbone shows accurate and high precision, while Yolov5-small has the shortest real inference time of detection and growth stages classification. Farmers can use the resulted deep learning model to detect, classify, and count weeds per growth stages automatically and as a result decrease not only the needed time and labor cost, but also the use of chemicals to control weeds on fields.11 0