3.6.5. YOLOv4’s Neck: Path Aggregation Network (PANet)

Path aggregation (Figure 7), originally proposed by Liu et al. [25], was used as the neck for YOLOv4 and YOLOv4-CSP in place of FPN (which was used in YOLOv3). This technique aggregates parameters from different backbone levels for different detector levels through bottom-up path augmentation and adaptive feature pooling. Bottom-up path augmentation shortens the information path and enhances the feature pyramid by making fine-grained localized information available to top layers (the classifiers). On the other hand, adaptive feature pooling recovers the broken information path between each proposal and all feature levels (cleaner paths are created). It fuses the information together from different layers using an element-wise max operation. Thus, PANet ensures that important features are not lost. For these reasons, PANet was used as the neck for YOLOv4 and YOLOv4-CSP.

Architecture of PANet, which inspired the path aggregation in YOLOv4’s neck. (a) FPN backbone; (b) bottom-up path augmentation; (c) adaptive feature pooling; (d) box branch; (e) fully-connected fusion (concatenation is done instead of addition for YOLOv4).