DeepAnomaly: Combining Background Subtraction and Deep Learning for Detecting Obstacles and Anomalies in an Agricultural Field

Sensors

卷:16

Kim A. Steen¹  Lars N. Nielsen²  Peter Christiansen³  Rasmus N. Jørgensen³  Henrik Karstoft³ 

[1] AgroIntelli, Aarhus 8200, Denmark;

[2] Danske Commodities, Aarhus 8000, Denmark;

[3] Department of Engineering, Aarhus University, Aarhus 8200, Denmark;

关键词: anomaly detection;    obstacle detection;    autonomous farming;    precision agriculture;    camera;    background subtraction;    change detection;    DeepAnomaly;   

DOI  :  10.3390/s16111904

来源: DOAJ

【 摘 要 】

Convolutional neural network (CNN)-based systems are increasingly used in autonomous vehicles for detecting obstacles. CNN-based object detection and per-pixel classification (semantic segmentation) algorithms are trained for detecting and classifying a predefined set of object types. These algorithms have difficulties in detecting distant and heavily occluded objects and are, by definition, not capable of detecting unknown object types or unusual scenarios. The visual characteristics of an agriculture field is homogeneous, and obstacles, like people, animals and other obstacles, occur rarely and are of distinct appearance compared to the field. This paper introduces DeepAnomaly, an algorithm combining deep learning and anomaly detection to exploit the homogenous characteristics of a field to perform anomaly detection. We demonstrate DeepAnomaly as a fast state-of-the-art detector for obstacles that are distant, heavily occluded and unknown. DeepAnomaly is compared to state-of-the-art obstacle detectors including “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks” (RCNN). In a human detector test case, we demonstrate that DeepAnomaly detects humans at longer ranges (45–90 m) than RCNN. RCNN has a similar performance at a short range (0–30 m). However, DeepAnomaly has much fewer model parameters and (182 ms/25 ms =) a 7.28-times faster processing time per image. Unlike most CNN-based methods, the high accuracy, the low computation time and the low memory footprint make it suitable for a real-time system running on a embedded GPU (Graphics Processing Unit).

【 授权许可】

Unknown