Dataset Information

Object Detection of Small Insects in Time-Lapse Camera Recordings.

ABSTRACT: 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.

SUBMITTER: Bjerge K

PROVIDER: S-EPMC10459366 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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Publications

Object Detection of Small Insects in Time-Lapse Camera Recordings.

Bjerge Kim K Frigaard Carsten Eie CE Karstoft Henrik H

Sensors (Basel, Switzerland) 20230818 16

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. ...[more]

PMID: 37631778

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Project description:Small object detection is one of the difficulties in the development of computer vision, especially in the case of complex image backgrounds, and the accuracy of small object detection still needs to be improved. In this article, we present a small object detection network based on YOLOv4, which solves some obstacles that hinder the performance of traditional methods in small object detection tasks in complex road environments, such as few effective features, the influence of image noise, and occlusion by large objects, and improves the detection of small objects in complex background situations such as drone aerial survey images. The improved network architecture reduces the computation and GPU memory consumption of the network by including the cross-stage partial network (CSPNet) structure into the spatial pyramid pool (SPP) structure in the YOLOv4 network and convolutional layers after concatenation operation. Secondly, the accuracy of the model on the small object detection task is improved by adding a more suitable small object detection head and removing one used for large object detection. Then, a new branch is added to extract feature information at a shallow location in the backbone part, and the feature information extracted from this branch is fused in the neck part to enrich the small object location information extracted by the model; when fusing feature information from different levels in the backbone, the fusion weight of useful information is increased by adding a weighting mechanism to improve detection performance at each scale. Finally, a coordinated attention (CA) module is embedded at a suitable location in the neck part, which enables the model to focus on spatial location relationships and inter-channel relationships and enhances feature representation capability. The proposed model has been tested to detect 10 different target objects in aerial images from drones and five different road traffic signal signs in images taken from vehicles in a complex road environment. The detection speed of the model meets the criteria of real-time detection, the model has better performance in terms of accuracy compared to the existing state-of-the-art detection models, and the model has only 44M parameters. On the drone aerial photography dataset, the average accuracy of YOLOv4 and YOLOv5L is 42.79% and 42.10%, respectively, while our model achieves an average accuracy (mAP) of 52.76%; on the urban road traffic light dataset, the proposed model achieves an average accuracy of 96.98%, which is also better than YOLOv4 (95.32%), YOLOv5L (94.79%) and other advanced models. The current work provides an efficient method for small object detection in complex road environments, which can be extended to scenarios involving small object detection, such as drone cruising and autonomous driving.

Dataset Information

Object Detection of Small Insects in Time-Lapse Camera Recordings.

Publications

Object Detection of Small Insects in Time-Lapse Camera Recordings.

Similar Datasets

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