In the autonomous driving environment, object instances in an image can be affected by various factors such as camera, driving state, weather, and system component. However, the deep learning‐based vision systems are vulnerable to perturbation, which contains noise. Thus, robust object detection under harsh autonomous‐driving environments is a more difficult than the generic situation. In this paper, it is found that not only the accuracy, but also the speed of the non‐maximum suppression‐based detector can be degraded under harsh environments. Therefore, object detection is handled under a harsh situation with adversarial mechanisms such as adversarial training and adversarial defence. Adversarial defence modules are designed to improve robustness in feature extraction level and define perturbations under a harsh environment for training object detectors to improve the robustness of the model's decision boundary. The proposed adversarial defence and training mechanisms improve the object detector in both accuracy and speed. The proposed method shows a 43.7% mean average precision for the COCO2015 dataset in generic object detection and 39.0% mean average precision for the BDD100K dataset in a driving environment. Furthermore, it achieves a real‐time capability of 23 frames per second.

The objective of this study was to improve user experience when appreciating visual artworks with soundscape music chosen by a deep neural network based on weakly supervised learning. We also propose a multi-faceted approach to measuring ambiguous concepts, such as the subjective fitness, implicit senses, immersion, and availability. We showed improvements in appreciation experience, such as the metaphorical and psychological transferability, time distortion, and cognitive absorption, with in-depth experiments involving 70 participants. Our test results were similar to those of “Bunker de Lumières: van Gogh”, which is an immersive media artwork directed by Gianfranco lannuzzi; the fitness scores of our system and “Bunker de Lumières: van Gogh” were 3.68/5 and 3.81/5, respectively. Moreover, the concordance of implicit senses between artworks and classical music was measured to be 0.88%, and the time distortion and cognitive absorption improved during the immersion. Finally, the proposed method obtained a subjective satisfaction score of 3.53/5 in the evaluation of its usability. Our proposed method can also help spread soundscape-based media art by supporting traditional soundscape design. Furthermore, we hope that our proposed method will help people with visual impairments to appreciate artworks through its application to a multi-modal media art guide platform.

The objective of robust object detection against multi-type corruption (MTC) is to improve detection performance in various domains, including common risks in real-world applications, such as noise, blur, adverse weather, digital errors, and more. In driving scenarios, MTC can occur due to harsh conditions like tunnels, extended driving periods, high temperatures, object movements, and even desert driving. However, addressing MTC poses a significant challenge as it can lead to catastrophic forgetting (CF) issues due to domain shifts. To enhance robustness without encountering CF, we propose the unified stem block for corruption (USB-C). This approach overcomes CF by utilizing complete edge (CE), which not only represents the external edges of objects but also captures internal information. In addition, we introduce the DC-Converter, facilitating efficient CE extraction under MTC conditions using lightweight parameter isolation. Our method not only enhances robustness without CF-induced performance degradation but also achieves over 20 FPS on a single consumer-grade GPU with minimal additional memory usage. The performances are demonstrated through experiments on COCO-C and Bdd100k benchmarks. Moreover, our method demonstrates a model-agnostic property as it can be applied to two representative detectors, RetinaNet and Faster R-CNN.