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This paper dives into the cutting-edge world of road asset detection on Thai highways, showcasing a novel approach that combines an upgraded REG model with Generalized Focal Loss. Our focus is on identifying key road elements—like pavilions, pedestrian bridges, information and warning signs, and concrete guardrails—to boost road safety and infrastructure management. While deep learning methods have shown promise, traditional models often struggle with accuracy in tricky conditions, such as cluttered backgrounds and variable lighting. To tackle these issues, we’ve integrated REG with Generalized Focal Loss, enhancing its ability to detect road assets with greater precision. Our results are impressive, the REGx model led the way with a mAP50 of 80.340, mAP50-95 of 60.840, precision of 79.100, recall of 76.680, and an F1-score of 77.870. These findings highlight the REGx model’s superior performance, demonstrating the power of advanced deep learning techniques to improve highway safety and infrastructure maintenance, even in challenging conditions.

Colorectal cancer is one of the leading causes of cancer death worldwide. As of now, colonoscopy is the most effective screening tool for diagnosing colorectal cancer by searching for polyps which can develop into colon cancer. The drawback of manual colonoscopy process is its high polyp miss rate. Therefore, polyp detection is a crucial issue in the development of colonoscopy application. Despite having high evaluation scores, the recently published methods based on fully convolutional network (FCN) require a very long inferring (testing) time that cannot be applied in a real clinical process due to a large number of parameters in the network. In this paper, we proposed a compressed fully convolutional network by modifying the FCN-8s network, so our network is able to detect and segment polyp from video images within a real-time constraint in a practical screening routine. Furthermore, our customized loss function allows our network to be more robust when compared to the traditional cross-entropy loss function. The experiment was conducted on CVC-EndoSceneStill database which consists of 912 video frames from 36 patients. Our proposed framework has obtained state-of-the-art results while running more than 7 times faster and requiring fewer weight parameters by more than 9 times. The experimental results convey that our system has the potential to support clinicians during the analysis of colonoscopy video by automatically indicating the suspicious polyps locations.

Semantic Segmentation is a fundamental task in computer vision and remote sensing imagery. Many applications, such as urban planning, change detection, and environmental monitoring, require the accurate segmentation; hence, most segmentation tasks are performed by humans. Currently, with the growth of Deep Convolutional Neural Network (DCNN), there are many works aiming to find the best network architecture fitting for this task. However, all of the studies are based on very-high resolution satellite images, and surprisingly; none of them are implemented on medium resolution satellite images. Moreover, no research has applied geoinformatics knowledge. Therefore, we purpose to compare the semantic segmentation models, which are FCN, SegNet, and GSN using medium resolution images from Landsat-8 satellite. In addition, we propose a modified SegNet model that can be used with remote sensing derived indices. The results show that the model that achieves the highest accuracy RGB bands of medium resolution aerial imagery is SegNet. The overall accuracy of the model increases when includes Near Infrared (NIR) and Short-Wave Infrared (SWIR) band. The results showed that our proposed method (our modified SegNet model, named RGB-IR-IDX-MSN method) outperforms all of the baselines in terms of mean F1 scores.

In this paper, we introduce an improved deep convolutional encoder-decoder network (DCED) for segmenting road objects from aerial images. Enhancements include the use of ELU (exponential linear unit) instead of ReLU, dataset augmentation with incrementally-rotated images to increase training data by eight times, and the use of landscape metrics to remove false road objects. Tested on the Massachusetts Roads dataset, our method outperformed the SegNet benchmark and other baselines in precision, recall, and F1 scores.

Road map extraction is vital for GIS and underpins many location-based applications like GPS navigation, delivery route planning, tourist attraction locating, and location-based marketing. This research uses satellite imagery, though other remotely sensed images like aerial photographs, UAVs, or drones are also applicable. Despite various proposed methods focusing primarily on accuracy, completeness of results is equally important. We enhance accuracy by incorporating connected component analysis and improve completeness using landscape metrics, which describe spatial characteristics through shape and isolation indices. Evaluated on precision, recall, quality, and F1 scores, our method achieves over 90% performance in all criteria.