The number of wheat ears per planting area is one of the key agronomic index to evaluate wheat yield. In the field scene, there are usually great differences in the shape, size and posture of wheat ears, and there are serious occlusion between leaves and ears and between ears and ears. At the same time, wheat awn, curly wheat leaves, weeds and uneven illumination introduced a lot of background interference. These complex factors led to a high false detection rate in traditional methods based on color and texture features. The detection method based on deep learning has a high missed detection rate for smallsize rice ear images in practical application. To solve these problems, a wheat ear detection method FCS R-CNN based on deep learning was proposed. Taking Cascade R-CNN as the basic network model, a feature pyramid network (FPN) was introduced to fuse shallow detailed features and highlevel rich semantic features, and online hard example mining (OHEM) technology was added to increase training frequency for highloss samples, the network was fused by the IOU threshold. Finally, a SVM classifier was trained based on the circular LBP texture features to carry out the reinspection of wheat ear detection results to further reduce the detection error. In the wheat field image test, the detection accuracy of FCS R-CNN model reached 92.9%, the average precision (AP) was 81.22%, the average time to identify a single image was 0.357s, and the AP was 21.76 percentage points higher than that of the original Cascade R-CNN. The results showed that the proposed method had better detection results for wheat ear detection in complex scenes, which provided a new idea for wheat yield estimation. 