# Digital Image Processing: Advanced Technologies and Applications
## 2 An Overview of Published Articles
The datasets were made thorough by introducing various aspects, for example, high- and low-density traffic images and distinct weather environments. Eventually, the improved YOLO-v5 algorithm becomes familiar to any traffic examples. Through fine-tuning the pre-trained system, the authors validated that the proposed YOLO-v5 has surpassed various traditional vehicle detection algorithms in terms of accuracy and complexity. The experiments were conducted on three different datasets to demonstrate its effectiveness in varying real-life conditions. The eleventh contribution in this Special Issue discusses segmentation in X-ray computed tomography (CT) data for non-destructive testing (NDT) by combining the segment anything model (SAM) with tile-based flood-filling networks (FFN). This method evaluates the performance of the SAM on volumetric NDT datasets and demonstrates its effectiveness to segment instances in challenging imaging scenarios. The authors implemented different methods to analyze the image-based SAM algorithm for use with volumetric datasets. This investigation enables the segmentation of 3D objects using FFN's spatial flexibility. The piecewise method for SAM influences FFN's abilities to segment various sized objects. This research has huge potential for merging SAM with FFN for volumetric instance segmentation, particularly for large objects. The twelfth contribution in this Special Issue discusses a novel methodology that combines bidirectional feature learning and generative networks to innovatively approach the domain adaptation problem. This study proves that merging bidirectional feature learning and generative networks is an effective solution for domain adaptation. Through various evaluations, authors verify that merging outperforms the existing works. The thirteenth contribution in this study proposes a fruit freshness classification method through deep learning. After the fruit data was gathered, the data was pre-processed, including augmentation and labeling. Later, the AlexNet model was used. Meanwhile, transfer learning and fine-tuning of the CNN was accomplished. Lastly, the Softmax classifier was used for classification. Experiments were performed using three commonly available datasets. The proposed model achieved highly favorable results in all three datasets by yielding an over 98% classification accuracy. In addition, this method is also computationally efficient and works in real-time to yield the final classification result. The fourteenth contribution is about a survey of optical character recognition (OCR). OCR is a process of extracting handwritten or printed text from a scanned or printed image and converting it to a machine-readable form for further data processing. OCR technology helps digitize documents for improved productivity and accessibility. Currently, the OCR is useful for preserving historical documents. The authors briefly discuss the recent OCR methods and identify the best-performing approach that researchers could utilize in their developed applications. This contribution also covers research gaps and presents future directions for Arabic language OCR in a systematic way. In the fifteenth contribution, the authors present a method of transfer style patterns while fusing the confined style construction with the local contented arrangement. In this contribution, numerous levels of coarse stylized features are reconstructed at low resolution using a coarse network. While achieving this, the color distribution is transferred, and the content structure is integrated with the initial style structure. Then, both the reconstructed and the content features are embraced to produce high-quality, structure-aware stylized images that have a high resolution. This is obtained through a fine network that has three structural selective fusion (SSF) sections. This method has proven to be robust by generating high-quality stylization outcomes.