## 3 Methodology
### Implementation Details
#### 3.2.3 FasterDetect  
To design faster neural networks, reducing the frequency of redundant computation and memory access is necessary to extract spatial features more efficiently. [PERSON] et al. [62] introduced a novel PConv technique called partial convolution. Its core idea relies on leveraging the redundancy in feature maps, applying conventional convolution only on a portion of the input channels for spatial feature extraction, and keeping the remaining channels unchanged. For continuous or regular memory access, the first or last continuous channels are considered representatives of the entire feature map for computation.  
Figure 4: Architecture of depthwise separable convolution.  
As illustrated in Figure 5, where the size of the feature map is \(h\times w\), \(c_{p}\) represents continuous network channels, and \(k\) is the filter. The FLOP calculation formula of PConv is shown as Equation (6):  
\[FLOPs_{PConv}=h\times w\times k^{2}\times c_{p}^{2} \tag{6}\]  
For a typical ratio (\(r=c_{p}/c=1/4\)), the FLOPs of PConv are only \(1/16\) of those of standard convolution. The formula for memory access computation is shown as Equation (7), where, in the case of (\(r=1/4\)), it is only one-quarter of the conventional convolution.  
\[FLOPs_{PConv}=h\times w\times 2c_{p}+k^{2}\times c_{p}^{2}\approx h\times w\times 2c _{p} \tag{7}\]  
To solve the shortcomings of inaccurate positioning and misclassification in complex scenes, we employ an anchorless design and decoupling head in the head of YOLOv8. This decoupled head not only independently handles classification and regression tasks, allowing the network to focus more on learning features, but it also significantly enhances the network's capacity to express and comprehend target information. We use PConv combined with the \(1\times 1\) convolution operation to reorganize the head of YOLOv8 to solve the problem of the large increase in the number of parameters and computational complexity due to network optimization, as shown in Figure 6.  
In the FasterDetect module designed, a series of operations are used to optimize the task processing flow. Firstly, a Split operation is adopted to divide the input vector, with one part entering PConv for feature processing and the other part directly connected to the subsequent operations. Secondly, one \(1\times 1\) convolution operation is employed for dimensionality reduction and the introduced nonlinearity, ultimately decoupling to separately address classification and regression tasks.  
The application of PConv works well with different parts of the input vector, further enabling a greater focus on learning the features required for a particular task. At the same time, the \(1\times 1\) convolution operation helps reduce the dimensionality and introduces addi  
Figure 5: Partial convolution architecture. The “_”_” symbol represents the convolution operation.  
Figure 6: Partial structure of FasterDetect.  
tional nonlinear transformations, giving the model greater flexibility and expressiveness. The final decoupling operation enables the network to handle classification and regression tasks more efficiently and enhances the performance of the network.  
Such an optimization design significantly reduces the FLOPs of the model and lowers the algorithm complexity, thereby improving the network running speed on edge devices.