Plastic waste is one of the main factors causing environmental pollution and affecting biodiversity, and identification and detecting plastic waste is the premise of removal and treatment. Unmanned aerial vehicle (UAV) is gradually applied to identify and classify plastic waste because of its advantages of simplicity, convenience, and safe operation, but the current visual interpretation method is inefficient and cumbersome. To support the detection of plastic waste, researchers have developed various automatic and semi-automatic algorithms. Among these algorithms, deep learning technology has outstanding performance in river garbage detection, but there are also practical problems such as small floating garbage volume, sparse samples, complex garbage environment, In this paper, a classification plus target detection (C+D) model is proposed, and a lightweight floating plastic waste detection model based on deep learning is constructed. The EfficientNet classification algorithm and Yolov5 target detection algorithm are combined and improved for experimental verification, and various floating plastic wastes are automatically identified and located. In this paper, the UAV image data set obtained from the flight in Longhe River Basin, Langfang City, Hebei Province, China, is used to investigate the plastic floating garbage. The algorithm verification experiment shows that the detection accuracy of the three kinds of plastic garbage is higher than 85% (AP: plastic bag: 0.95; Plastic foam: 0.90; Plastic bottle: 0.87), which shows its excellent floating plastic recognition ability. The FPS of UAV equipment can reach 40.23 on edge, which shows that its recognition speed is fast and meets the real-time demand.