Real-Time Object Detection with Automatic Switching between Single-Board Computers and the Cloud

We present a wireless real-time object detection system utilizing single-board devices, cloud computing platforms and web-streaming. Currently, most inference applications stat- ically perform tasks either on local machines or remote cloud servers. However, devices connected through cellular technolo- gies face volatile network conditions, compromising detection performance. Furthermore, while the limited computing power of single-board computers degrade detection correctness, exces- sive power consumption of machine learning models used for inference reduces operation time. In this paper, we propose a dynamic system that monitors embedded device’s wireless link quality and battery level to decide on detecting objects locally or remotely. The experimental results show that our dynamic offloading approach could reduce devices’ energy usage while achieving high accuracy, real-time object detection. Index Terms—Machine learning, WebRTC, object detection

Sensitivity of Robot-Aided Remote Object Detection in Forests under Variation of Light Illumination

Forests degradation and deforestation are increasingly becoming a risk to the world’s ecosystem with major effects on climate change. Mitigating these dangers is tackled through reliable management of monitoring tree species, insect infestations and wildlife behaviour. Although forest rangers can use artificial intelligence and machine learning techniques to analyse forest health through visionary sensing, exploring the accuracy of object detection under low illuminations such as sunsets, clouds or below dense forest canopy is often ignored. In this paper, we have investigated the importance of illumination on detection through a high definition GoPro9 camera as compared to the low-cost RaspberryPi camera. An external sensing platform accommodated by a quadruped robot is developed to carry the hardware, one of the first implementations of autonomous system in forest health monitoring. The compound-scaled object detection, YOLOv5s model pretrained on COCO dataset containing 800,000 instances, used for person detection, is retrained on custom dataset to detect forest health indicators such as burrows and deadwood. The system is tested and evaluated under various lighting conditions to detect objects located at various distances from the vision sensors. This study concludes that YOLOv5s model can detect a person and forest health indicators up to a distance of 10m with accuracy of 67% and 51% respectively at dusk which shows that light exposure has a major effect on detection performance. Furthermore, the quadruped robot carrying the sensing platform managed to successfully shift between different positions while carrying out the detection

Global economic burden of unmet surgical need for appendicitis

Background: There is a substantial gap in provision of adequate surgical care in many low-and middle-income countries. This study aimed to identify the economic burden of unmet surgical need for the common condition of appendicitis. Methods: Data on the incidence of appendicitis from 170 countries and two different approaches were used to estimate numbers of patients who do not receive surgery: as a fixed proportion of the total unmet surgical need per country (approach 1); and based on country income status (approach 2). Indirect costs with current levels of access and local quality, and those if quality were at the standards of high-income countries, were estimated. A human capital approach was applied, focusing on the economic burden resulting from premature death and absenteeism. Results: Excess mortality was 4185 per 100 000 cases of appendicitis using approach 1 and 3448 per 100 000 using approach 2. The economic burden of continuing current levels of access and local quality was US $92 492 million using approach 1 and$73 141 million using approach 2. The economic burden of not providing surgical care to the standards of high-income countries was $95 004 million using approach 1 and$75 666 million using approach 2. The largest share of these costs resulted from premature death (97.7 per cent) and lack of access (97.0 per cent) in contrast to lack of quality. Conclusion: For a comparatively non-complex emergency condition such as appendicitis, increasing access to care should be prioritized. Although improving quality of care should not be neglected, increasing provision of care at current standards could reduce societal costs substantially

Pooled analysis of WHO Surgical Safety Checklist use and mortality after emergency laparotomy

Background The World Health Organization (WHO) Surgical Safety Checklist has fostered safe practice for 10 years, yet its place in emergency surgery has not been assessed on a global scale. The aim of this study was to evaluate reported checklist use in emergency settings and examine the relationship with perioperative mortality in patients who had emergency laparotomy. Methods In two multinational cohort studies, adults undergoing emergency laparotomy were compared with those having elective gastrointestinal surgery. Relationships between reported checklist use and mortality were determined using multivariable logistic regression and bootstrapped simulation. Results Of 12 296 patients included from 76 countries, 4843 underwent emergency laparotomy. After adjusting for patient and disease factors, checklist use before emergency laparotomy was more common in countries with a high Human Development Index (HDI) (2455 of 2741, 89.6 per cent) compared with that in countries with a middle (753 of 1242, 60.6 per cent; odds ratio (OR) 0.17, 95 per cent c.i. 0.14 to 0.21, P <0001) or low (363 of 860, 422 per cent; OR 008, 007 to 010, P <0.001) HDI. Checklist use was less common in elective surgery than for emergency laparotomy in high-HDI countries (risk difference -94 (95 per cent c.i. -11.9 to -6.9) per cent; P <0001), but the relationship was reversed in low-HDI countries (+121 (+7.0 to +173) per cent; P <0001). In multivariable models, checklist use was associated with a lower 30-day perioperative mortality (OR 0.60, 0.50 to 073; P <0.001). The greatest absolute benefit was seen for emergency surgery in low- and middle-HDI countries. Conclusion Checklist use in emergency laparotomy was associated with a significantly lower perioperative mortality rate. Checklist use in low-HDI countries was half that in high-HDI countries.Peer reviewe

Global variation in anastomosis and end colostomy formation following left-sided colorectal resection

Background End colostomy rates following colorectal resection vary across institutions in high-income settings, being influenced by patient, disease, surgeon and system factors. This study aimed to assess global variation in end colostomy rates after left-sided colorectal resection. Methods This study comprised an analysis of GlobalSurg-1 and -2 international, prospective, observational cohort studies (2014, 2016), including consecutive adult patients undergoing elective or emergency left-sided colorectal resection within discrete 2-week windows. Countries were grouped into high-, middle- and low-income tertiles according to the United Nations Human Development Index (HDI). Factors associated with colostomy formation versus primary anastomosis were explored using a multilevel, multivariable logistic regression model. Results In total, 1635 patients from 242 hospitals in 57 countries undergoing left-sided colorectal resection were included: 113 (6·9 per cent) from low-HDI, 254 (15·5 per cent) from middle-HDI and 1268 (77·6 per cent) from high-HDI countries. There was a higher proportion of patients with perforated disease (57·5, 40·9 and 35·4 per cent; P < 0·001) and subsequent use of end colostomy (52·2, 24·8 and 18·9 per cent; P < 0·001) in low- compared with middle- and high-HDI settings. The association with colostomy use in low-HDI settings persisted (odds ratio (OR) 3·20, 95 per cent c.i. 1·35 to 7·57; P = 0·008) after risk adjustment for malignant disease (OR 2·34, 1·65 to 3·32; P < 0·001), emergency surgery (OR 4·08, 2·73 to 6·10; P < 0·001), time to operation at least 48 h (OR 1·99, 1·28 to 3·09; P = 0·002) and disease perforation (OR 4·00, 2·81 to 5·69; P < 0·001). Conclusion Global differences existed in the proportion of patients receiving end stomas after left-sided colorectal resection based on income, which went beyond case mix alone

Performance of MobileNetV3 Transfer Learning on Handheld Device-based Real-Time Tree Species Identification

Detailed information on tree species constitutes an essential factor to support forest health monitoring and biodiversity conservation. Current deep learning-based mobile applications for tree and plant identification require excessive computation. They largely depend on a network connection to perform computing tasks on powerful remote servers in the Cloud. Many forestry areas are remote with limited or no cellular coverage, which is an obstacle for these applications to recognize trees and plants in these areas in real-time. This paper investigates existing CNN-based machine learning applications for plant identification tailored for handheld device usages. Driven by network independence, reduced computation, size and time requirements, we propose the use of MobileNet (a mobile computer vision architecture) transfer learning to improve the accuracy of offline leaf-based plant recognition. We then carry out experimental validation of state-of-the-art MobileNet. Our findings reveal that using MobileNetV3 transfer learning, accuracy up to 90% can be achieved within fewer iterations than end-to-end CNN-based models for plant identification. The lightweight model comes with reduced computation that runs independently within a smartphone application without internet access, ideal for tree species identification in rural forests

Low Latency and Non-Intrusive Accurate Object Detection in Forests

A resilient and healthy forest requires accurate and timely monitoring, observing key forest health indicators (FHI). Forest managers and rangers usually perform tedious manual data collection using citizen science for biodiversity conservation and ecological research. With the advent of faster radio network technologies such as 4G, it is advantageous to leverage these networks’ high speed and low latency for real-time monitoring. We present a novel approach to stream high definition videos over cellular networks to provide real-time (< 0.5 seconds) data transmission to the YOLOv5 machine learning algorithm, hosted in the cloud. The system provides non-intrusive precise tree class detection, matching existing models such as Fast R-CNN and SSD. Our investigation also reveals that the same accuracy can be achieved with 99% fewer iterations, minimizing computational time and cost

Sensitivity of Robot-Aided Remote Object Detection in Forests under Variation of Light Illumination

Forests degradation and deforestation are increasingly becoming a risk to the world’s ecosystem with major effects on climate change. Mitigating these dangers is tackled through reliable management of monitoring tree species, insect infestations and wildlife behaviour. Although forest rangers can use artificial intelligence and machine learning techniques to analyse forest health through visionary sensing, exploring the accuracy of object detection under low illuminations such as sunsets, clouds or below dense forest canopy is often ignored. In this paper, we have investigated the importance of illumination on detection through a high definition GoPro9 camera as compared to the low-cost RaspberryPi camera. An external sensing platform accommodated by a quadruped robot is developed to carry the hardware, one of the first implementations of autonomous system in forest health monitoring. The compound-scaled object detection, YOLOv5s model pretrained on COCO dataset containing 800,000 instances, used for person detection, is retrained on custom dataset to detect forest health indicators such as burrows and deadwood. The system is tested and evaluated under various lighting conditions to detect objects located at various distances from the vision sensors. This study concludes that YOLOv5s model can detect a person and forest health indicators up to a distance of 10m with accuracy of 67% and 51% respectively at dusk which shows that light exposure has a major effect on detection performance. Furthermore, the quadruped robot carrying the sensing platform managed to successfully shift between different positions while carrying out the detection