# Deep Learning-Based Portable Image Analysis System for Real-Time Detection of _Vespa velutina_
[PERSON]  
1 Department of Agricultural Engineering, National Institute of Agricultural Sciences,  
Jeongju 54876, Republic of Korea; sud116867@naver. Busan, where it was first discovered. Among approximately 480,000 requests to remove wasp nests from 2010-2014 in South Korea, the requests to remove _V. velutina_ nests were the highest among those for all Vespa species [8]. The venom of _V. velutina_ is lethal not only to insects but also to humans. Since the invasion of _V. velutina_, two deaths have been reported in France and South Korea each [9]. In addition, _V. velutina_ is known for extensively preying on honeybees (especially _Apis mellifera_), syphid flies, social wasps, and other pollinators. Their presence at honeybee hives can suppress foraging activity, leading to decreased honey yields and increased winter mortality. Additionally, their intense predation of insects on flowers can negatively impact the overall pollination of plants [6]. Moreover, as more than 85% of _V. velutina's_ prey consists of honeybees, the increase in its population has led to a significant rise in damage to Korean beekeepers [10]. It is all the more devastating as honeybees are unable to defend themselves against _V. velutina's_ hunting strategy of preying on individual bees, which implies that even a single _V. velutina_ can cause considerable damage to apariaries [11; 12]. The methods used in Korean apariaries to control wasp populations include pesticide repartition and vespa traps; however, these methods do not specifically target _V. velutina_. Meanwhile, research on using honeybee extracts to lure and capture _V. velutina_ individuals and queen bees to reduce their number has proved to be effective because some queen bees build nests called \"embryo nests,\" where the population can grow to thousands of individuals [13; 14; 15]. However, some _V. velutina_ individuals cannot be lured in this way. To address this problem, a real-time image data analysis system was developed to immediately notify users of the presence of _V. velutina_ to enable a prompt response. In the past, object recognition using image data was performed using computer vision-based local feature-matching methods that classified objects or located their positions. This involved developers selecting feature points and extracting feature vectors using local scale-invariant feature detection methods. Recognition rates were improved using a support vector machine, which is one of the early machine learning methods [16; 17; 18; 19]. Unlike these classical machine learning methods, deep learning algorithms do not require selecting feature points to optimize feature extraction and classification. With the emergence of convolutional neural networks (CNNs), spatial information in image data are maintained while simultaneously optimizing feature extraction and classification. This has led to the development of several deep learning algorithms, including AlexNet, GoogLeNet, and you only look once (YOLO). CNN-based deep learning models have outperformed classical machine learning methods in object recognition using image data [20; 21; 22]. The YOLOv1 algorithm used in this study was first introduced in 2015. The speed of YOLOv1 was improved in YOLOv2 using Darknet-19 as the backbone, and its performance was improved using various methods such as batch normalization, high-resolution classification, convolution with anchor boxes, dimension clusters, and direct location prediction. YOLOv3 changed the backbone to Darknet-53 and improved the detection rate of small objects using the predicting across scales method. YOLOv4 was then released as a result of further experiments and applications, including a bag of freebies, to achieve higher accuracy, and YOLOv5 is the current version [23; 24; 25]. As new versions of the YOLO algorithm were released, the detection performance for small objects improved, and the algorithm became capable of detecting multiple objects simultaneously. In general, _V. velutina_ appears smaller than people or objects in image data.