# Infrared Dim and Small Target Detection Based on Local-Global Feature Fusion
## 3 Experiments
### Experiments and Analysis
#### 3.2.2 Comparison Experiments on Drone Vehicle  
We compared the proposed infrared dim and small target detection algorithm with the following representative algorithms in this field: infrared patch-image (IPI) [31]; non-convex rank approximation minimization (NRAM) [33]; non-convex optimization with Lp-norm constraint (NOLC) [34]; partial sum of the tensor nuclear norm (PTSNN) [35], based on low-rank and sparse matrices; tri-layer local contrast measure (TLLCM) [36], based on the human vision system, which transforms target salience into a local optimization problem; new top-hat, based on mathematical morphology; local intensity and gradient properties (LIG) [37] based on vector fields; SSD; receptive field block net (RFBnet) [38]; and infrared small-target detection U-net (ISTRU-net) [39], based on deep learning. The parameters for each method are set according to their optimal experimental results, with detailed parameter settings shown in Table 4.  
The visualization results of different methods for various scenes and different local signal-to-noise ratio sequences are shown in Figure 7. The detected targets are marked with red rectangles, while false alarms are unmarked. To evaluate the detection efficiency of various algorithms, Table 5 shows the detection time of each algorithm on different data sequences, providing an intuitive demonstration of the real-time performance of these algorithms. NOLC, NRAM, PSTNN, and IPI, which belong to low-rank sparse algorithms, generally exhibit moderate speeds. Additionally, IPI employs global fixed weights, which contributes to an increase in computation time. TLLCM requires a lengthy computation time due to its reliance on calculating locally related features. Although the new top-hat has a fast detection speed, it is characterized by a low detection rate and a high false alarm rate. LIG faces challenges in enhancing algorithm speed due to the time required for calculating multi-scale direction diversity. With sufficient computational resources, our proposed algorithm outperforms other deep learning algorithms, boasting the shortest average detection time across all datasets. It also shows an improvement of more than 16.3% in detection time compared to RFBnet, demonstrating superior real-time processing capability. In the experiment, the model's inference speed on the PC is approximately 57 frames per second (FPS). In scenarios that require real-time processing, such as drone target detection and aerial monitoring, the algorithm can be deployed on field programmable gate arrays (FPGA) to achieve real-time processing capabilities, though several challenges remain. This is part of our ongoing research, and we aim to further optimize the model and increase the processing speed to meet real-time demands. \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Parameter** \\ \hline IPI & Patch size: 45, step: 10 \\ NRAM & Patch size: 50, step: 10 \\ TLLCM & GS: [1 2 1;2 4 2; 1 2 1]/16, local window size: 3 \\ PTSNN & Patch size: 20, step: 20, \(\lambda_{\text{L}}=0.5\) \\ New Top-Hat & Inner scale: 4, outer scale: 9 \\ LIG & \(k=0.2\), Patch size: N = 19 \\ NOLC & Patch size: 30, step: 10, \(p\):0.6 \\ SSD & Iter: 10,000, batch size: 64, LR = 0.0001 \\ RFBnet & Epoch: 300, batch size: 32, LR: 0.0004 \\ ISTRU-net & Epoch: 500, batch size: 48, LR: 0.0001 \\ Proposed & Epoch: 300, batch size: 64, LR = 0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameter settings of different methods. Figure 7: _Cont._