## 4 Experiment and Analysis
### Ablation Experiments  
Ablation experiments are conducted in this paper based on the improved backbone network module, FPN module, and loss function. AP (Average Precision) is used as the model accuracy index, FPS (Frame per Second) is used as the model real-time performance index, and the baseline for comparison is the original YOLACT model with ResNet-101 as the backbone network. The experimental results are presented in Tables 1-3, which test the accuracy and speed of the model based on each improvement point, as well as the  
Figure 8: Comparison of segmentation results between original model and the proposed method. Figure 9: Comparison of bounding box and segmentation mask. boxes for the instance targets, the Protonet module outputs the prototype mask images, and the two branches work in parallel and independently, and the location of the bbox will not affect the generation of the prototype mask. While the use of fewer anchors limits the performance of the Prediction module, the Protonet is unaffected, so the mAP of bbox decreases more than the mask mAP. After using feature fusion and introducing an ECA attention mechanism into the FPN module, the overall output of the FPN is optimized, resulting in a significant improvement in the accuracy of the final mask and bounding box. The RIoU Loss function improves the network performance while only affecting the training speed, so it will not reduce the speed performance of the network. Although these improvements in accuracy are based on increasing the additional computational load of the network, these additional computational loads do not have a significant impact due to the model being lightweight. Compared with the original network, the improved network only loses 3.30% of the bounding box accuracy, but increases the mask accuracy by 5.82% and the detection speed by 37.4 FPS. In order to verify the effectiveness of the improved FPN module and RIoU Loss function, experiments are also conducted on the ResNet-101 backbone network, and the results are shown in Table 2.  
Similar to the data in Table 1, it is also shown in Table 2 that the use of the improved FPN module and RIoU Loss could both improve the accuracy performance of the model based on the ResNet-101 backbone network. But without a lightweight structure, the real-time performance is limited, as the improved FPN module increases the inference time of the model. In a scenario with low real-time performance requirements, the ResNet-based model can also be used to achieve a higher accuracy. Figure 11 shows a comparison of the AP\({}_{\text{S}}\), AP\({}_{\text{M}}\), and AP\({}_{\text{L}}\) between the original YOLACT and the improved model in this paper. AP\({}_{\text{S}}\) is the average precision for small objects (area \(<32\times 32\) pixels), while AP\({}_{\text{M}}\) is for medium objects (32 \(\times\) 32 pixels \(<\) area \(<\)96 \(\times\) 96 pixels) and AP\({}_{\text{L}}\) is for large objects (area \(>\) 96 \(\times\) 96 pixels). In Figure 11, it can be seen that the average precision of the bounding boxes of objects in all sizes is decreased, which is caused by the joint action of the lightweight backbone network and other improvement points in this paper. Since the proposed model in this paper improves the input feature map of Protonet, although the accuracy of bboxes is reduced, the segmentation accuracy of the network for targets of various sizes is still improved. Table 3 provides a more detailed comparison of the time consumption after the introduction of each module. As the model improvements in this paper are carried out in a more lightweight way, the overall time consumption added to the network is less, and it will not significantly affect the real-time performance of the model. Furthermore, since we use fewer channels in the backbone and detection module, the time for both modules is significantly reduced. The increased time consumption of the model is mainly concentrated in the FPN module and object detection module.