# A Fast Maritime Target Identification Algorithm for Offshore Ship Detection
## 3 Methodology
### Multi-Scale Feature Fusion  
Scholars strove to find better feature fusion methods for the greater robustness of information. The initial development of the target detector was used to obtain the whole logical information of the object by a single layer for making prediction judgments. For example, the last layer's output was adopted for subsequent processing in a series of R-CNN.  
A typical representative application of multiscale feature fusion is FPN [40]. The multiscale information obtained from feature fusion and improved the network performance for different scale targets (including tiny targets).  
YOLOF involves two key modules of a projector and residual blocks. In the projector, \(1\times 1\) convolution is applied to reduce the number of parameters, and then \(3\times 3\) convolution  
Figure 1: Schematic diagram of (**A**). _IOU_ and (**B**). _GIOU_.  
is done to extract contextual semantic information (similar to FPN). Residual blocks are four residual modules with different rates of dilation stacked to generate output features with multiple fields of perception, in Figure 2. For residual blocks, all convolution layers are followed by a BatchNorm layer [41] and a ReLU layer [42], but just convolution layers and BatchNorm layers are used in Projector. To accept varying target sizes, 4 consecutive residual units are employed to allow the integration of numerous features with different perceptual fields in a one-level feature.  
An encoder called Single Feature Map Fusion (SFMF) is presented here because it has been developed as the key component of the detector, distinguished from a feature pyramid based on multiple maps. It was obtained from the optimizations of YOLOF, to design the featured fusion components upon a single feature layer [38]. By the residual module, the YOLOF encoder obtains semantic information on multiple scales.  
In Figure 3, L1-L5 is generated on the backbone paths with feature maps containing different scale information, path-1 integrates the results of L1-L4 and L5. The results of path-1 produce the final outcome P5. Remarkably, it ignores the preprocessing. In practice, the use of ReLU in a backbone network may result in the loss of information about the destination. This study tries to employ Meta-ACON [43] (refer to Section 3.3), which is employed in the backbone network to learn to activate or inactivate automatically.  
Figure 2: Encoder of YOLOF.  
Preliminary validation of the fusion path method is tested in the 2007-COCO dataset, and the results are shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Fusion Path** & **AP** \\ \hline SMSF & 38.5 \\ PANet [44] & 37.8 \\ BiFPN [1,45] & 37.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of Fusion Paths.  
Figure 3: SFMF Architecture.  
Consideration of additional channels or whether to use shortcuts results are shown in Table 2.  
The shortcut retains the original information and overwrites all scale targets in YOLOF. The SFMF retains the lower-scale information for subsequent fusion. The results indicate that the SFMF can create better results with shortcuts.