基于改进Faster RCNN的节肢动物目标检测方法

自然生态环境下的节肢动物目标检测存在目标背景复杂、尺度差异大以及目标密集等特点，导致目标检测精度和准确率不高。为此，提出一种基于改进Faster RCNN模型的节肢动物目标检测方法 AROD RCNN。首先，设计一种有监督的并行空间与通道注意力（SPSCAT）机制，以提高复杂背景环境下节肢动物目标检测的准确率；然后，引入第二代可变形卷积重塑ResNet50中C1～C5块卷积层，并使用特征金字塔网络（FPN）对ResNet50中C2～C6块进行特征融合以解决目标尺度差异较大影响检测精度的问题；最后，通过密集局部回归（DLR）方法对回归阶段进行改进，从而提高模型回归的准确性。实验结果表明，该方法在ArTaxOr数据集上的各类别平均精度（mAP）达到了0.717，较原始Faster RCNN模型提高了0.453，而召回率达到了0.787。可见该方法能够有效解决目标遮挡和复杂背景等问题，在节肢动物密集目标与小目标检测中表现良好。

Arthropod object detection method based on improved Faster RCNN

Arthropod object detection in natural environment has characteristics of complex object background， large scale difference， and dense objects，resulting in poor object detection accuracy and precision. Therefore， an arthropod object detection method was proposed based on the improved Faster RCNN model， namely AROD RCNN （ARthropod Object Detection RCNN）. Firstly， a Supervised Parallel mechanism with Spatial and Channel ATtention modules （SPSCAT） was designed to improve the accuracy of arthropod object detection in the environment with complex background. Then， the second-generation deformable convolution was introduced to reconstruct the convolutional layer with C1~C5 blocks in ResNet50， and the Feature Pyramid Network （FPN） was adopted to perform feature fusion on the C2~C6 blocks in ResNet50 to solve the problem that large difference in object scale affected detection accuracy. Finally， the Dense Local Regression （DLR） method was used to improve the regression stage， thereby improving the accuracy of the model regression. Experimental results show that on ArTaxOr （Arthropod Taxonomy Orders Object Detection） dataset， the proposed method has the mean Average Precision （mAP） of 0.717， which is 0.453 higher than that of the original model， and has the recall reached 0.787. It can be seen that the proposed method can effectively solve the problems of object occlusion and complex background， and performs well in the detection of dense arthropod objects and small arthropod objects.