To achieve automated harvesting of hydroponic Chinese flowering cabbage, the detection and localization of the cabbage are crucial. This study proposes a two stages detection and localization algorithm for hydroponic Chinese flowering cabbage, which includes macro-detection and micro-localization. The macro-detection algorithm is named P-YOLOv5s-GRNF. Its improvement strategies include adopting pruning techniques, the GSConv, receptive field attention convolution (RFAConv), normalization-based attention module (NAM), and the Focal-EIOU Loss module. The micro-localization algorithm is named YOLOv5s-SBC. Its improvement strategies include adding a 160×160 detection layer, removing a 20×20 detection layer, introducing a weighted bidirectional feature pyramid network (BiFPN) structure, and utilizing the coordinate attention (CA) mechanism. The experimental results showed that P-YOLOv5s-GRNF increased the mAP(mean average precision) by 0.8%, 4.3%, 3.2%, 0.7%, 19.3%, 9.8%, 3.1% compared to mainstream object detection algorithms YOLOv5s, YOLOv6s, YOLOv7-tiny, YOLOv8s, YOLOv5s-Shufflenetv2, YOLOv5s-Mobilenetv3, YOLOv5s-Ghost, respectively. Compared to the original model, P-YOLOv5s-GRNF decreased parameters by 18%, decreased model size to 11.9MB, decreased FLOPs to 14.5G, and increased FPS by 4.3. YOLOv5s-SBC also increased mAP by 4.0% compared to the original YOLOv5s, with parameters decreased by 65%, model size decreased by 60%, and FLOPs decreased to 15.3G. Combined with a depth camera, the improved models construct a positioning system that can provide technical support for the automated and intelligent harvesting of Chinese flowering cabbage.