UAVs-based Small Object Detection and Tracking in Various Complex Scenarios

Zu, Shicheng; Yang, Kai; Wang, Xiu-Lai; Yu, Zhongzheng; Hu, Yawen; Jia, Lianshun

doi:10.1145/3459104.3459141

Cited by 2 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With higher frame rates, such as those in video recordings [ 42 ], information in the temporal dimension can be used to improve the detection and tracking of moving objects [ 43 , 44 ]. The detection of small moving objects is an important research area with respect to flying insects, surveillance of honeybee colonies, and tracking the movement of insects.…”

Section: Introductionmentioning

confidence: 99%

Object Detection of Small Insects in Time-Lapse Camera Recordings

Bjerge,

Frigaard,

Karstoft

2023

Sensors

View full text Add to dashboard Cite

As pollinators, insects play a crucial role in ecosystem management and world food production. However, insect populations are declining, necessitating efficient insect monitoring methods. Existing methods analyze video or time-lapse images of insects in nature, but analysis is challenging as insects are small objects in complex and dynamic natural vegetation scenes. In this work, we provide a dataset of primarily honeybees visiting three different plant species during two months of the summer. The dataset consists of 107,387 annotated time-lapse images from multiple cameras, including 9423 annotated insects. We present a method for detecting insects in time-lapse RGB images, which consists of a two-step process. Firstly, the time-lapse RGB images are preprocessed to enhance insects in the images. This motion-informed enhancement technique uses motion and colors to enhance insects in images. Secondly, the enhanced images are subsequently fed into a convolutional neural network (CNN) object detector. The method improves on the deep learning object detectors You Only Look Once (YOLO) and faster region-based CNN (Faster R-CNN). Using motion-informed enhancement, the YOLO detector improves the average micro F1-score from 0.49 to 0.71, and the Faster R-CNN detector improves the average micro F1-score from 0.32 to 0.56. Our dataset and proposed method provide a step forward for automating the time-lapse camera monitoring of flying insects.

show abstract

Section: Introductionmentioning

confidence: 99%