# Multi-Fusion Approach for Wood Microscopic Images Identification Based on Deep Transfer Learning
## 4 Experimental Results and Discussion
### Impact of Improvement Strategies on the Test Accuracy  
This paper conducts a series of ablation experiments to explore the impact of improved strategy configuration parameters and improved strategy combinations on the accuracy of the test. First, due to the diversity of the SPP module structure in the receptive field enhancement strategy and the incompatibility of module parameters between different tasks, four model configurations were introduced for experimental comparative analysis, denoted as SPP-A, SPP-A#, SPP-B, SPP-B#. The SPP-A configuration draws on the YOLOv3 detection algorithm [19], performs three different scales of maximum pooling on the input feature map, and adds a shortcut branch, which maintains the spatial resolution by zero-filling during maximum pooling. The SPP-B configuration is derived from the semantic segmentation network PSPNet [20], consisting of five branches, of which four are the maximum pooling of different scales, and the other is the direct connection of the input. The parameter configuration details: the pooling core size of SPP-A is \(13\ast 13\), \(25\ast 25\), \(37\ast 37\); the pooling core size of SPP-A# is 5\(\ast\)5, 9\(\ast\)9, 13\(\ast\)13; the pooling core size of SPP-B is \(5\ast 5\), 9\(\ast\)9, 13\(\ast\)13, \(30\ast 30\); the pooling core size of SPP-B# is \(10\ast 10\), \(20\ast 20\), \(30\ast 30\), \(60\ast 60\). The detection accuracy of the receptive field enhancement strategy SPP module on the test set is shown in Table 2. In Table 2, \"shortcut\" represents the direct connection of the input end, and \"extra branch\" represents whether to introduce the fourth maximum pooling branch. There is a fourth branch by default under the SPP-B configuration. The first four rows of data in the table reflect the influence of additional branches and direct input terminals on the configuration of A. It can be found that adding additional branches and direct input terminals can bring certain performance improvements (0.4% and 1.2%), and the effect is more obvious with the shortcut improvements. However, the cooperative use of the two did not bring about the expected performance improvement, which may be caused by the in-adaptability of multiple receptive field combinations to the current problem target scale. In addition, by observing the test accuracy of the model before and after parameter adjustment, it can be concluded that it is very necessary for the model to be properly adjusted on a given network. It can better extract different scale information in the current network feature map. For example, 3.1% in the SPP-A configuration and 2.4% in the SPP-B configuration. It is worth noting that the parameter adjustment principle of the module proposed in the improved model is that the pooling operation covers the entire feature map space scale as much as possible. In the task solved in this paper, the scale of the feature map after down sampling the backbone network is \(38*38\). Secondly, this paper conducts ablation experiments on each improvement strategy proposed to evaluate its influence on the generalization ability and adaptability of the model. The experimental results are shown in Table 3. The ablation experiments were carried out on six models, respectively. \"Model 0\" represents the baseline method, as the standard Faster RCNN model without any improvement strategy; \"Model 1\" to \"Model 3\" represent the individual improvement strategies introduced Model; \"Model 4\" to \"Model 5\" represent a combination model of improvement strategies. From the above Table 3, it can be seen that the spatial pyramid pooling SPP strategy in the \"Model 1\" receptive field enhancement module has improved significantly, from 77.8% to 80.1%. This module expands the experience field of standard convolution operations through the hole convolution with different expansion rates and the maximum pooling operation with different pooling kernel sizes. The network not only pays attention to