FieldSAFE: Dataset for Obstacle Detection in Agriculture

Kragh, Mikkel Fly; Christiansen, P. A.; Laursen, Morten Stigaard; Larsen, Morten Andreas Dahl; Steen, Kim Arild; Green, Ole; Karstoft, Henrik; Jørgensen, Rasmus Nyholm

doi:10.3390/s17112579

Cited by 58 publications

(52 citation statements)

References 24 publications

(23 reference statements)

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“…Stereo camera images, thermal camera images and Lidar point cloud data are recorded on grassland, under varying lighting conditions and distances. Kragh et al [118] presents a multimodal dataset for obstacle detection in agriculture containing 2h of raw sensor data from a tractor-mounted sensor system in a grass mowing scenario, including moving humans scattered in the field.…”

Section: A Pedestrian Datasetsmentioning

confidence: 99%

Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior

Camara

Bellotto

Coşar

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases such as cars at pedestrian crossings and off-carriageway cases such as delivery vehicles navigating through crowds on pedestrianized high-streets. Unlike static and kinematic obstacles, pedestrians are active agents with complex, interactive motions. Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behaviour as well as detecting and tracking them. This narrative review article is Part II of a pair, together surveying the current technology stack involved in this process, organising recent research into a hierarchical taxonomy ranging from lowlevel image detection to high-level psychological models. This selfcontained Part II covers the higher levels of this stack, consisting of models of pedestrian behaviour, from prediction of individual pedestrians' likely destinations and paths, to game-theoretic models of interactions between pedestrians and autonomous vehicles. This survey clearly shows that, although there are good models for optimal walking behaviour, high-level psychological and social modelling of pedestrian behaviour still remains an open research question that requires many conceptual issues to be clarified. Early work has been done on descriptive and qualitative models of behaviour, but much work is still needed to translate them into quantitative algorithms for practical AV control.

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Section: A Pedestrian Datasetsmentioning

confidence: 99%

Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior

Camara

Bellotto

Coşar

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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“…The publicly available FieldSAFE dataset (Kragh et al, 2017 ) for multi-modal obstacle detection in agricultural fields was used for the evaluation. The dataset includes 2 h of recording during mowing of a grass field in Denmark.…”

Section: Discussionmentioning

confidence: 99%

“…The sensor suite presented by Kragh et al ( 2017 ) was used to record multi-modal sensor data. The dataset has recently been made publicly available.…”

Section: Methodsmentioning

confidence: 99%

Multi-Modal Detection and Mapping of Static and Dynamic Obstacles in Agriculture for Process Evaluation

et al. 2018

Self Cite

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Today, agricultural vehicles are available that can automatically perform tasks such as weed detection and spraying, mowing, and sowing while being steered automatically. However, for such systems to be fully autonomous and self-driven, not only their specific agricultural tasks must be automated. An accurate and robust perception system automatically detecting and avoiding all obstacles must also be realized to ensure safety of humans, animals, and other surroundings. In this paper, we present a multi-modal obstacle and environment detection and recognition approach for process evaluation in agricultural fields. The proposed pipeline detects and maps static and dynamic obstacles globally, while providing process-relevant information along the traversed trajectory. Detection algorithms are introduced for a variety of sensor technologies, including range sensors (lidar and radar) and cameras (stereo and thermal). Detection information is mapped globally into semantical occupancy grid maps and fused across all sensors with late fusion, resulting in accurate traversability assessment and semantical mapping of process-relevant categories (e.g., crop, ground, and obstacles). Finally, a decoding step uses a Hidden Markov model to extract relevant process-specific parameters along the trajectory of the vehicle, thus informing a potential control system of unexpected structures in the planned path. The method is evaluated on a public dataset for multimodal obstacle detection in agricultural fields. Results show that a combination of multiple sensor modalities increases detection performance and that different fusion strategies must be applied between algorithms detecting similar and dissimilar classes.

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“…There are some open source datasets for obstacle detection in agriculture like FieldSAFE [ 22 ]. FieldSAFE just contains humans in an agricultural environment, but not in paddy field.…”

Section: Methodsmentioning

confidence: 99%

Vision-Based Moving Obstacle Detection and Tracking in Paddy Field Using Improved Yolov3 and Deep SORT

Qiu

Zhao

Zhou

et al. 2020

Sensors

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Using intelligent agricultural machines in paddy fields has received great attention. An obstacle avoidance system is required with the development of agricultural machines. In order to make the machines more intelligent, detecting and tracking obstacles, especially the moving obstacles in paddy fields, is the basis of obstacle avoidance. To achieve this goal, a red, green and blue (RGB) camera and a computer were used to build a machine vision system, mounted on a transplanter. A method that combined the improved You Only Look Once version 3 (Yolov3) and deep Simple Online and Realtime Tracking (deep SORT) was used to detect and track typical moving obstacles, and figure out the center point positions of the obstacles in paddy fields. The improved Yolov3 has 23 residual blocks and upsamples only once, and has new loss calculation functions. Results showed that the improved Yolov3 obtained mean intersection over union (mIoU) score of 0.779 and was 27.3% faster in processing speed than standard Yolov3 on a self-created test dataset of moving obstacles (human and water buffalo) in paddy fields. An acceptable performance for detecting and tracking could be obtained in a real paddy field test with an average processing speed of 5–7 frames per second (FPS), which satisfies actual work demands. In future research, the proposed system could support the intelligent agriculture machines more flexible in autonomous navigation.

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FieldSAFE: Dataset for Obstacle Detection in Agriculture

Cited by 58 publications

References 24 publications

Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior

Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior

Multi-Modal Detection and Mapping of Static and Dynamic Obstacles in Agriculture for Process Evaluation

Vision-Based Moving Obstacle Detection and Tracking in Paddy Field Using Improved Yolov3 and Deep SORT

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