In 2021, compared to the last year, global port container logistic volumes increased by 7% (857 million 20-foot equivalent units (TEU)), while deliveries of newly built container ships increased by 18% (10,929 thousand gross tons) [1]. Hence, the problem instigated by an increased volume of logistics and port congestion can be solved through the efficient operation of the port, and ship detection and volume data processing have become important. Ship monitoring related to port logistics is possible by checking the Auto Identification System (AIS) [2]. The Terrestrial AIS (T-AIS) system is capable of detecting only short-range communication up to 50 nautical miles [3], and there is an omission to monitor the ship when the navigator controls the transmission-receiving of AIS [4]. Therefore, as the ability of satellites increases, there is a movement to detect missing ships using satellite images [5]. In this study, ship detection was conducted to monitor container ships that were not operate in the anchorage area around the port or berth at the pier. In order to find container ships through deep learning, four types of vessels (container ships, bulk ships, oil tankers, and small ships) were extracted from Compact Advanced Satellite 500-1 (CAS500-1) satellite images in the yellow sea and one port to make a training dataset. The CAS500 satellites carry different sensors for different Earth observation missions. CAS500-1 has the panchromatic and multispectral modes using the AEISS-C (Advanced Earth Imaging Sensor System) payload for land surface imagery, land topography imagery, and vegetation-type imagery. Although improvement in remote surface reflectance is needed, it can be beneficial at sea. The detection model was established through YOLOv5 (You Only Look Once version 5) [6] deep learning algorithm. The model's performance was evaluated by applying them to the CAS500-1, and Korea Multi-Purpose Satellite-3 (KOMPSAT-3), taken at the Busan New Port, Korea, and Oakland...