This paper addresses the critical challenge of vehicle detection in the harsh winter conditions in the Nordic regions, characterized by heavy snowfall, reduced visibility, and low lighting. Due to their susceptibility to environmental distortions and occlusions, traditional vehicle detection methods have struggled in these adverse conditions. The advanced proposed deep learning architectures brought promise, yet the unique difficulties of detecting vehicles in Nordic winters remain inadequately addressed. This study uses the Nordic Vehicle Dataset (NVD), which has UAV images from northern Sweden, to evaluate the performance of state-of-the-art vehicle detection algorithms under challenging weather conditions. Our methodology includes a comprehensive evaluation of single-stage, two-stage, and transformer-based detectors against the NVD. We propose a series of enhancements tailored to each detection framework, including data augmentation, hyperparameter tuning, transfer learning, and novel strategies designed explicitly for the DETR model. Our findings not only highlight the limitations of current detection systems in the Nordic environment but also offer promising directions for enhancing these algorithms for improved robustness and accuracy in vehicle detection amidst the complexities of winter landscapes. The code and the dataset are available at https://nvd.ltu-ai.dev.

The purpose of this study is to investigate the development process for Artificial inelegance (AI) and machine learning (ML) applications in order to provide the best support environment. The main stages of ML are problem understanding, data management, model building, model deployment and maintenance. This project focuses on investigating the data management stage of ML development and its obstacles as it is the most important stage of machine learning development because the accuracy of the end model is relying on the kind of data fed into the model. The biggest obstacle found on this stage was the lack of sufficient data for model learning, especially in the fields where data is confidential. This project aimed to build and develop a framework for researchers and developers that can help solve the lack of sufficient data during data management stage. The framework utilizes several data augmentation techniques that can be used to generate new data from the original dataset which can improve the overall performance of the ML applications by increasing the quantity and quality of available data to feed the model with the best possible data. The framework was built using python language to perform data augmentation using deep learning advancements.

Vehicle detection and recognition in drone images is a complex problem that has been used for different safety purposes. The main challenge of these images is captured at oblique angles and poses several challenges like non-uniform illumination effect, degradations, blur, occlusion, loss of visibility, etc. Additionally, weather conditions play a crucial role in causing safety concerns and add another high level of challenge to the collected data. Over the past few decades, various techniques have been employed to detect and track vehicles in different weather conditions. However, detecting vehicles in heavy snow is still in the early stages because of a lack of available data. Furthermore, there has been no research on detecting vehicles in snowy weather using real images captured by unmanned aerial vehicles (UAVs). This study aims to address this gap by providing the scientific community with data on vehicles captured by UAVs in different settings and under various snow cover conditions in the Nordic region. The data covers different adverse weather conditions like overcast with snowfall, low light and low contrast conditions with patchy snow cover, high brightness, sunlight, fresh snow, and the temperature reaching far below -0 degrees Celsius. The study also evaluates the performance of commonly used object detection methods such as Yolo v8, Yolo v5, and fast RCNN. Additionally, data augmentation techniques are explored, and those that enhance the detectors' performance in such scenarios are proposed. The code and the dataset will be available at https://nvd.ltu-ai.dev

Vehicle detection in real-time scenarios is challenging because of the time constraints and the presence of multiple types of vehicles with different speeds, shapes, structures, etc. This paper presents a new method relied on generating a confidence map-for robust and faster vehicle detection. To reduce the adverse effect of different speeds, shapes, structures, and the presence of several vehicles in a single image, we introduce the concept of augmentation which highlights the region of interest containing the vehicles. The augmented map is generated by exploring the combination of multiresolution analysis and maximally stable extremal regions (MR-MSER). The output of MR-MSER is supplied to fast CNN to generate a confidence map, which results in candidate regions. Furthermore, unlike existing models that implement complicated models for vehicle detection, we explore the combination of a rough set and fuzzy-based models for robust vehicle detection. To show the effectiveness of the proposed method, we conduct experiments on our dataset captured by drones and on several vehicle detection benchmark datasets, namely, KITTI and UA-DETRAC. The results on our dataset and the benchmark datasets show that the proposed method outperforms the existing methods in terms of time efficiency and achieves a good detection rate.