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.