基于嵌入式YOLO的玉米苗与杂草检测轻量化模型BACD-YOLO研究OA

Aiming at the problems of complex field environment and difficulty in taking into account the accuracy of weed identification and detection efficiency in the critical window period of 2～5 leaf weeding of maize,a lightweight detection model BACD-YOLO for maize seedlings and weeds based on improved YOLO v8n was proposed.Using Adamax optimizer to enhance the robustness of model in field environment;the weighted bidirectional feature pyramid network(BiFPN)was introduced to the feature fusion network as the connection layer to improve the detection effect of the model on weeds with different growth.Adopting lightweight down sampling module(Adown)replaced the conventional convolution in the network to reduce the amount of parameter calculation of model redundancy;the coordinate attention(CA)mechanism was embedded in the SPPF layer and the feature fusion network to improve the positioning ability of the model for small targets and densely distributed weeds;DualConv lightweight double convolution was used to replace the ordinary convolution structure in original model to further realize the lightweight of the model and the detection ability of the model for weeds with similar characteristics.The experimental results showed the accuracy,recall and average accuracy of improved model were 86.6%,86.2%and 91.2%,respectively,which were 2.2,1.5 and 1.6 percentage points higher than that of the original model,and floating-point calculation and parameter quantity were only 6.2×109 and 2.3×106,which were 23.5%and 23.3%lower than that of original model,respectively.According to the verification test results,the improved model was more suitable for edge device deployment application with high detection accuracy,strong generalization ability and excellent lightweight performance.The frame rate was 19.4 f/s,and the detection accuracy was 86.6%,which can meet requirements of field real-time detection.The research result can provide an effective lightweight solution for accurate identification of corn seedlings and weeds and robot weeding.