Automated Region of Interest-Based Data Augmentation for Fallen Person Detection in Off-Road Autonomous Agricultural Vehicles.

Due to the global population increase and the recovery of agricultural demand after the COVID-19 pandemic, the importance of agricultural automation and autonomous agricultural vehicles is growing. Fallen person detection is critical to preventing fatal accidents during autonomous agricultural vehicle operations. However, there is a challenge due to the relatively limited dataset for fallen persons in off-road environments compared to on-road pedestrian datasets. To enhance the generalization performance of fallen person detection off-road using object detection technology, data augmentation is necessary. This paper proposes a data augmentation technique called Automated Region of Interest Copy-Paste (ARCP) to address the issue of data scarcity. The technique involves copying real fallen person objects obtained from public source datasets and then pasting the objects onto a background off-road dataset. Segmentation annotations for these objects are generated using YOLOv8x-seg and Grounded-Segment-Anything, respectively. The proposed algorithm is then applied to automatically produce augmented data based on the generated segmentation annotations. The technique encompasses segmentation annotation generation, Intersection over Union-based segment setting, and Region of Interest configuration. When the ARCP technique is applied, significant improvements in detection accuracy are observed for two state-of-the-art object detectors: anchor-based YOLOv7x and anchor-free YOLOv8x, showing an increase of 17.8% (from 77.8% to 95.6%) and 12.4% (from 83.8% to 96.2%), respectively. This suggests high applicability for addressing the challenges of limited datasets in off-road environments and is expected to have a significant impact on the advancement of object detection technology in the agricultural industry.

StaticPigDet: Accuracy Improvement of Static Camera-Based Pig Monitoring Using Background and Facility Information.

The automatic detection of individual pigs can improve the overall management of pig farms. The accuracy of single-image object detection has significantly improved over the years with advancements in deep learning techniques. However, differences in pig sizes and complex structures within pig pen of a commercial pig farm, such as feeding facilities, present challenges to the detection accuracy for pig monitoring. To implement such detection in practice, the differences should be analyzed by video recorded from a static camera. To accurately detect individual pigs that may be different in size or occluded by complex structures, we present a deep-learning-based object detection method utilizing generated background and facility information from image sequences (i.e., video) recorded from a static camera, which contain relevant information. As all images are preprocessed to reduce differences in pig sizes. We then used the extracted background and facility information to create different combinations of gray images. Finally, these images are combined into different combinations of three-channel composite images, which are used as training datasets to improve detection accuracy. Using the proposed method as a component of image processing improved overall accuracy from 84% to 94%. From the study, an accurate facility and background image was able to be generated after updating for a long time that helped detection accuracy. For the further studies, improving detection accuracy on overlapping pigs can also be considered.

Short-Term Success Rates of Smoking Cessation Support Programs and Factors Predicting Smoking Relapse: Using Data from a Smoking Cessation Clinic in a Hospital.

BACKGROUND: Although the number of medical institutions running a smoking cessation clinic is on the rise, there remains a paucity of research on the long- and short-term success rates of smoking cessation programs, as well as on smoking relapse rates, before and after project implementation. This study assessed the general characteristics of patients visiting the smoking cessation clinic, success rate of smoking cessation in the short term, and risks of relapse. METHODS: Medical records from March 2015 to April 2017 were analyzed and telephone surveys were conducted with 151 smokers who visited a hospital smoking cessation clinic from March 2015 to April 2017. RESULTS: Of the 139 smokers who were eligible for follow-up, 22 (15.8%) failed to quit smoking initially. The clinic's 6-month success rate of smoking cessation was 64.83%. Those with higher medication compliance had a lower risk of primary failure (odds ratio, 0.056; 95% confidence interval, 0.005-0.609), whereas those with higher age (hazard ratio [HR], 0.128; P=0.0252) and a greater number of visits to the clinic (HR, 0.274; P=0.0124) had a lower risk of relapsing. CONCLUSION: The risk of primary failure to quit was higher with low medication compliance, and that of relapsing was higher with lower age and fewer number of clinic visits. Various evaluation and analysis methods can be carried out in the future based on the accumulated data for maintenance of smoking cessation and relapse prevention.