Improving the performance of object detection by preserving label distribution

Object detection is a task that performs position identification and label classification of objects in images or videos. The information obtained through this process plays an essential role in various tasks in the field of computer vision. In object detection, the data utilized for training and validation typically originate from public datasets that are well-balanced in terms of the number of objects ascribed to each class in an image. However, in real-world scenarios, handling datasets with much greater class imbalance, i.e., very different numbers of objects for each class , is much more common, and this imbalance may reduce the performance of object detection when predicting unseen test images. In our study, thus, we propose a method that evenly distributes the classes in an image for training and validation, solving the class imbalance problem in object detection. Our proposed method aims to maintain a uniform class distribution through multi-label stratification. We tested our proposed method not only on public datasets that typically exhibit balanced class distribution but also on custom datasets that may have imbalanced class distribution. We found that our proposed method was more effective on datasets containing severe imbalance and less data. Our findings indicate that the proposed method can be effectively used on datasets with substantially imbalanced class distribution.Comment: Code is available at https://github.com/leeheewon-01/YOLOstratifiedKFold/tree/mai

Human Following in Mobile Platforms with Person Re-Identification

Human following is a crucial feature of human-robot interaction, yet it poses numerous challenges to mobile agents in real-world scenarios. Some major hurdles are that the target person may be in a crowd, obstructed by others, or facing away from the agent. To tackle these challenges, we present a novel person re-identification module composed of three parts: a 360-degree visual registration, a neural-based person re-identification using human faces and torsos, and a motion tracker that records and predicts the target person's future position. Our human-following system also addresses other challenges, including identifying fast-moving targets with low latency, searching for targets that move out of the camera's sight, collision avoidance, and adaptively choosing different following mechanisms based on the distance between the target person and the mobile agent. Extensive experiments show that our proposed person re-identification module significantly enhances the human-following feature compared to other baseline variants

Reinterpret 4As framework of energy security from the perspective of human security – an analysis of China’s electric vehicle (EV) development

This research addresses two issues: expanding the understanding of human security with the case of China’s electric vehicle (EV) development and examining the human security implications of China’s EV development. This research adopts an online ethnographic method to record very personal driving forces and barriers to China’s EV uptake through the experiences shared by ordinary Chinese people. From a theoretical perspective, this research provides more evidence for the applicability of the broad human security approach in energy security analysis through the case of China’s EV development. By reinterpreting the 4As framework (availability, affordability, accessibility, and acceptability), which is one of the most frequently adopted frameworks in the analysis of energy security on the state level, (Cherp & Jewell, 2014, p. 416), this research challenges the current understanding of human security by demonstrating that threats to human security exist at all levels of development and touch not only the most vulnerable but also people living in well-developed regions in the face of the lasted technological transformation. The analysis of China’s EV development as a strategic energy security consideration sheds some light on the complicated relationship between state and individual security within China’s security discussion. It enriches the understanding of human security by exploring how it has been adapted to the Chinese social and political context. Meanwhile, drawing on the insights from ontological security through the lens of some key indicators (protection, autonomy, and social acceptance), this research emphasises the necessity of incorporating the subjective dimension in human security analysis to capture subjective feelings and psychological factors in everyday security. This research contributes empirically to identifying human security implications of EV development based on the real-life experiences shared by the Chinese people, which may constitute barriers to China’s EV uptake. Informed by the flexible interpretation of security agency offered by the broad human security approach, this research demonstrates that apart from the state’s dominant position as the main security provider, other players, such as carmakers, also play an important role in shaping people’s perceptions of how secure EVs are. Recognising that the misoperation of an automobile can cause serious physical harm to both those on board and other road users, this research argues that ordinary people should not be only considered as the object of protection but also as the agent with the power to exert influence on the security implications of the new technology

TractorEYE: Vision-based Real-time Detection for Autonomous Vehicles in Agriculture

Agricultural vehicles such as tractors and harvesters have for decades been able to navigate automatically and more efficiently using commercially available products such as auto-steering and tractor-guidance systems. However, a human operator is still required inside the vehicle to ensure the safety of vehicle and especially surroundings such as humans and animals. To get fully autonomous vehicles certified for farming, computer vision algorithms and sensor technologies must detect obstacles with equivalent or better than human-level performance. Furthermore, detections must run in real-time to allow vehicles to actuate and avoid collision.This thesis proposes a detection system (TractorEYE), a dataset (FieldSAFE), and procedures to fuse information from multiple sensor technologies to improve detection of obstacles and to generate a map. TractorEYE is a multi-sensor detection system for autonomous vehicles in agriculture. The multi-sensor system consists of three hardware synchronized and registered sensors (stereo camera, thermal camera and multi-beam lidar) mounted on/in a ruggedized and water-resistant casing. Algorithms have been developed to run a total of six detection algorithms (four for rgb camera, one for thermal camera and one for a Multi-beam lidar) and fuse detection information in a common format using either 3D positions or Inverse Sensor Models. A GPU powered computational platform is able to run detection algorithms online. For the rgb camera, a deep learning algorithm is proposed DeepAnomaly to perform real-time anomaly detection of distant, heavy occluded and unknown obstacles in agriculture. DeepAnomaly is -- compared to a state-of-the-art object detector Faster R-CNN -- for an agricultural use-case able to detect humans better and at longer ranges (45-90m) using a smaller memory footprint and 7.3-times faster processing. Low memory footprint and fast processing makes DeepAnomaly suitable for real-time applications running on an embedded GPU. FieldSAFE is a multi-modal dataset for detection of static and moving obstacles in agriculture. The dataset includes synchronized recordings from a rgb camera, stereo camera, thermal camera, 360-degree camera, lidar and radar. Precise localization and pose is provided using IMU and GPS. Ground truth of static and moving obstacles (humans, mannequin dolls, barrels, buildings, vehicles, and vegetation) are available as an annotated orthophoto and GPS coordinates for moving obstacles. Detection information from multiple detection algorithms and sensors are fused into a map using Inverse Sensor Models and occupancy grid maps. This thesis presented many scientific contribution and state-of-the-art within perception for autonomous tractors; this includes a dataset, sensor platform, detection algorithms and procedures to perform multi-sensor fusion. Furthermore, important engineering contributions to autonomous farming vehicles are presented such as easily applicable, open-source software packages and algorithms that have been demonstrated in an end-to-end real-time detection system. The contributions of this thesis have demonstrated, addressed and solved critical issues to utilize camera-based perception systems that are essential to make autonomous vehicles in agriculture a reality

Abstracts from the 50th European Society of Human Genetics Conference: Posters

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