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Honeybees pollinate over 75% of the total food resources produced annually, and they produce valuable hive products, such as bee pollen, propolis, and royal jelly. However, species such as the Asian hornet (<i>Vespa velutina</i>) feed on more than 85% of honeybees, causing a decline in their population and considerable damage to beekeepers in Korea. To prevent damage to honeybees, a portable real-time monitoring system was developed that detects <i>V. velutina</i> individuals and notifies users of their presence. This system was designed with a focus on portability and ease of installation, as <i>V. velutina</i> can be found in various areas of apiary sites. To detect <i>V. velutina</i>, an improved convolutional neural network YOLOv5s was trained on 1960 high-resolution (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>3840</mn><mo>×</mo><mn>2160</mn></mrow></semantics></math></inline-formula>) image data. At the confidence threshold of ≥0.600 and intersection over the union of ≥0.500, the performance of the system in terms of detection accuracy, precision, recall, F1 score, and mean average precision was high. A distance-based performance comparison showed that the system was able to detect <i>V. velutina</i> individuals while monitoring three beehives. During a field test of monitoring three beehives, the system could detect 83.3% of <i>V. velutina</i> during their hunting activities and send alarms to registered mobile application users.