## 4 Results and Discussion
### Instance Segmentation Results  
In this subsection, we show the result of instance segmentation on the Cityscapes validation set. In the SOLOv2 network, the total loss function consists of category loss and mask loss. As shown in Figure 5, with the increase of network iterations, the loss function of each method gradually tended to be dynamically stable, and finally the instance segmentation models were obtained. As shown in Table 4, the instance segmentation method containing AFPM achieves the best performance. More specifically, AP gets 2.7% improvement and 9.5% improvement rate over baseline as well as AP\({}_{50}\) gets 2.4% improvement and 4.6% improvement rate over baseline, which demonstrates that the proposed module can efficaciously assist the performance of instance segmentation. Although the proposed architecture makes the parameter increase slightly, which is 0.53 million, the average precision of the instance  
Figure 5: The loss function curve of different methods. (**a**) The loss curve of baseline; (**b**) the loss curve of baseline with CBAM; (**c**) the loss curve of baseline with CBAM and CTAM; (**d**) the loss curve of baseline with AFPM; (**e**) the total loss curve of baseline and baseline with AFPM. segmentation algorithm improves obviously. We proved the effectiveness of each step by ablation experiments. Explicitly, we initially adopted SOLOv2 [28] with FPN [15] as backbone to conduct baseline experiments. The AP value and AP\({}_{50}\) value of base-network was 28.3% and 51.7%, respectively. When we added convolutional triplet attention module (CTAM) to baseline, we got 28.7% AP value and 52.0% AP\({}_{50}\) value. Then we added convolutional block attention module (CBAM) on the basic network and got 30.2% AP value as well as 52.5% AP\({}_{50}\) value, which made the overall average precision was significantly promoted. Subsequently, we employed CTAM to the model which involved CBAM and the AP\({}_{50}\) value increased effectively from 52.5% to 53.3% with negligible parameters. On this basis, we included branches that incorporate low-level features to construct the proposed framework and achieved 31.0% AP value as well as 54.1% AP\({}_{50}\) value. In Table 5, we report in detail the average precision of different methods on 8 instance class. By adding CBAM to the feature extraction structure, the redundant features extracted by the multi-level network are well filtered and the effectiveness of network parameters is improved, which makes the performance of instance segmentation method significantly boosted on all instance categories, especially in person class and rider class. Through combining lateral connections with CTAM, the high-level semantic features between bottom-up and top-down pathways are efficiently propagated. Among them, bus class and train class are enhanced by 0.9% and 0.7%, respectively. In the method in which AFPM is applied, the bus class with AP of 52.5% and car class with AP of 50.9% perform well. Furthermore, compared with the base-network, bicycle class and person class obtain higher improvement rate, which are 23.1% and 14.5%, respectively. Moreover, as shown in Figure 6, we qualitatively display the segmentation results of ablation experiments. In Figure 6b, the basic network has the problems of missing and false detection in instance segmentation. For example, the network mistakenly segmented the car in the right edge of the first image when multiple objects overlap and failed to correctly identify the bicycle in the second image as well as the car in the right middle of the third image. As shown in Figure 6c, when CBAM and CTAM are integrated to the baseline, the problems mentioned above are powerfully improved and more object contours are detected and segmented. For instance, the distant bicycle in the first image and the car in the third image are accurately identified, and the outline of the truck in the second image is roughly segmented compared with the base-network.