Detecting missed tassels is crucial for assessing the quality of aritificial emasculation in maize seed production fields. Aiming at the problems of large parameter quantity, low detection efficiency and poor accuracy of the existing maize tassel detection models, a lightweight tassel detection model based on RTMDet-tiny, named MLCE-RTMDet, was proposed. The model used the lightweight MobileNetv3 as the feature extraction network to effectively reduce the model parameters. The CBAM attention module in the neck network was integrated to enhance multi-scale feature extraction capability for tassel objects, overcoming potential performance losses caused by the lightweight networks. Simultaneously, the EIOU Loss was adopted, replacing the GIOU Loss, which further improved the accuracy of tassel detection. Experiments on the self-built dataset showed that the improved MLCE-RTMDet model reduced model parameters to 3.9×106, while the number of floating point operations was lowered to 5.3×109, resulting in a 20.4% reduction in parameters and a 34.6% decrease in computational complexity compared with that of the original model. When evaluated on the test set, the model’s mean average precision (mAP) reached 92.2%, reflecting a 1.2 percentage points improvement over the original model. The inference speed was increased to 41.9 frames per second (FPS), representing a 12.6% enhancement. Compared with current mainstream detection models such as YOLO v6, YOLO v8, and YOLO X, MLCE-RTMDet demonstrated superior overall detection performance. The improved high-accuracy lightweight model offered technical support for tassel re-inspection and emasculation quality assessment in maize seed production fields following artificial emasculation.