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Due to the rich genetic diversity of maize germplasm resources, the size, morphological structure and color of tassels were quite different. The resolution of maize tassel image collected by UAV equipped with visible light sensor was lower than that of ground acquisition, and some tassels in the image were too small, which were highly similar to the background, occluded and interlaced. The above factors led to low accuracy of tassel detection. Therefore, a tassel detection method for maize germplasm resources based on improved YOLO v7-tiny model was proposed. This method enhanced the model’s ability to extract tassel features by introducing SPD-Conv module and VanillaBlock module into YOLO v7-tiny, and adding ECA-Net module. Tested on the self-built tassel dataset of maize germplasm resources, the mean average precision of the improved YOLO v7-tiny was 94.6%, which was 1.5 percentage points higher than that of YOLO v7-tiny, and 1.0 percentage points and 3.1 percentage points higher than that of the lightweight models YOLO v5s and YOLO v8s, respectively. This method significantly reduced the occurrence of missing tassels and false detection of background as tassels in the image, and effectively reduced the misdetection of a single tassel as multiple tassels and the number of tassels in interlaced state. The model size of the improved YOLO v7-tiny was 17.8MB, and the inference speed was 231f/s. The proposed method can improve the accuracy of tassel detection under the premise of ensuring the lightweight of the model, and can provide technical support for the real-time and accurate detection of tassel of maize germplasm resources.