Low-resolution (LR) imagery poses significant challenges to object detection systems, particularly in autonomous and resource-constrained environments where bandwidth and sensor quality are limited. To address this issue, this paper presents an integrated framework that enhances object detection performance by incorporating a Super-Resolution (SR) preprocessing stage prior to detection. Specifically, a Dense Residual Connected Transformer (DRCT) is employed to reconstruct high-resolution (HR) images from LR inputs, effectively restoring fine-grained structural and textural information essential for accurate detection. The reconstructed HR images are subsequently processed by a YOLOv11 detector without requiring architectural modifications. Experimental evaluations demonstrate consistent improvements across multiple scaling factors, with an average increase of 13.4% in Mean Average Precision (mAP)@50 at ×2 upscaling and 9.7% at ×4 compared with direct LR detection. These results validate the effectiveness of the proposed SR-based preprocessing approach in mitigating the adverse effects of image degradation. The proposed method provides an improved yet computationally challenging solution for object detection.

High-quality images are crucial for navigation, obstacle detection, and environmental understanding, but transmitting high-resolution images over constrained networks presents significant challenges. This study introduces an image transmission system using super-resolution convolutional neural networks (SRCNN) to enhance image quality without increasing bandwidth requirements by transmitting low-resolution images and upscaling them with SRCNN. The first phase of the research involved data collection, in which information was acquired directly from an appropriate locus to produce training, validation, and testing datasets. The second, three SRCNN models (915, 935, and 955) were trained using such a training dataset. The last was an evaluation, in which model 915 showed quick learning and stable performance with initial high loss, while model 935 had rapid convergence but potential overfitting. Model 955 achieved high initial performance. Three SRCNN model configurations were tailored to the specific needs of autonomous electric vehicles operating in limited areas, such as the locus. Input image resolution ranged from 128×128 pixels to 256×256 pixels, while output resolution varied from 256×256 pixels to 512×512 pixels. These resolutions can be acceptable for efficient image transmission over IEEE 802.11ac, but on the long range (LoRa) network, it still produces some delay.

Image super-resolution (SR) is a fundamental yet challenging task in computer vision, aiming to reconstruct high-resolution (HR) images from low-resolution (LR) inputs. To address these limitations, this paper proposes a novel super-resolution framework that integrates several advanced architectural components to enhance image reconstruction quality. At its core, the framework introduces a Dense Residual Dense Block (DRDB), which incorporates residual dense connectivity and dual-pathway processing to effectively extract and fuse both low- and high-level features. This design improves feature representation and mitigates gradient vanishing in deeper networks. To enhance flexibility and visual consistency, Adaptive Instance Normalization (AdIN) is employed to dynamically align channel-wise feature statistics while preserving structural information. Additionally, a Channel Attention Block (CAB) with a Squeeze-and-Excitation (CAB-SE) mechanism is embedded to adaptively recalibrate channel-wise and spatial feature importance, allowing the network to focus on salient regions. Experimental results on standard SR benchmarks demonstrate that the proposed method outperforms existing approaches in terms of texture reconstruction, visual fidelity, and computational efficiency. Overall, this work presents a scalable and effective solution for deep super-resolution tasks, offering improved generalization, adaptability, and feature enhancement through synergistic architectural integration.

Accurate vehicle detection in complex traffic environments remains a challenging task, particularly when dealing with low-resolution imagery and small-scale objects such as distant vehicles and pedestrians. Limitations in image quality, caused by hardware constraints, transmission noise, and real-time processing requirements, often lead to significant performance degradation in conventional object detection frameworks. To address these challenges, this paper proposes an integrated super-resolution–assisted vehicle detection framework that combines a transformer-based SR preprocessing module with a YOLOv12-based object detector. The proposed SR module is designed to exploit global contextual information and long-range dependencies, enabling more faithful reconstruction of fine-grained structural details that are critical for small-object recognition. The enhanced high-resolution representations are subsequently fed into the YOLOv12 detector, which offers a favorable trade-off between detection accuracy and computational efficiency across multiple object scales. Extensive experiments conducted on multiple benchmark datasets, including VEDAI-VISIBLE, VEDAI-IR, DOTA, and xView, demonstrate that the proposed approach consistently outperforms existing CNN- and GAN-based SR methods as well as baseline detection models. Quantitative and qualitative results confirm notable improvements in detection accuracy, particularly for small and densely distributed vehicles, while maintaining practical inference efficiency. These findings highlight the effectiveness of integrating transformer-based SR as a preprocessing stage for robust vehicle detection, contributing to more reliable intelligent transportation and traffic monitoring systems.