Abstract
Wheat is one of the most productive crops in the world, and its planting density plays an important role in the estimation of wheat yield. In the face of complex backgrounds such as dense wheat ears and leaf interference in the wheat ear planting environment, traditional target detection methods have problems such as high detection costs and poor performance. Improve wheat ear detection performance in complex backgrounds, a proposed improved network based on YOLOX-S is called YOLOX-Wheat. The Mish activation function is used to replace the original activation function to reduce loss and improve generalization ability and introduce the large core attention LKA, establish the correlation of different parts, improve the network learning representation quality and information acquisition ability, and improve the detection performance. Compared with the original YOLOX, AP@0.5:0.95, AP@0.5, AP@0.75, and AR@0.5:0.95 have increased by 2.8 per cent, 1.4 per cent, 4.3 per cent, and 2.5 per cent.
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