## 4 Experiments, Results, and Discussion
### Experimental Results
#### 4.3.1 Horizontal Object Detection
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**mAP**Models** & **YOLOX-s** & **YOLOX-m** & **YOLOX-l** & **YOLOX-x** & **DCE** & **DCE** & **DCE** & **DCE** \\  & & & & & & **YOLOX-s** & **YOLOX-m** & **YOLOX-l** & **YOLOX-x** \\ \hline small-vehicle & 0.5320 & 0.4412 & 0.6301 & 0.5458 & 0.7402 & 0.7319 & **0.7853** & 0.7518 \\ large-vehicle & 0.8312 & 0.8186 & 0.8655 & 0.8081 & 0.8732 & 0.8781 & **0.8903** & 0.8729 \\ plane & 0.8953 & 0.9042 & 0.9013 & 0.9005 & 0.9019 & **0.9049** & 0.9024 & 0.9041 \\ storage-rank & 0.7367 & 0.7883 & 0.7698 & 0.7852 & 0.8351 & **0.8715** & 0.8673 & 0.8686 \\ ship & 0.8442 & 0.8929 & 0.8597 & 0.8558 & 0.8964 & **0.9001** & 0.8865 & 0.8975 \\ harbor & 0.8103 & 0.8647 & 0.8418 & 0.8251 & 0.8645 & 0.8803 & 0.8799 & **0.8816** \\ ground-track-field & 0.7224 & 0.7864 & 0.7769 & 0.7747 & 0.7299 & 0.7808 & 0.7801 & **0.8063** \\ soccer-ball-field & 0.7234 & 0.7858 & 0.7621 & 0.7646 & 0.8112 & 0.8348 & 0.8125 & **0.8389** \\ tennis-count & 0.9043 & 0.9064 & 0.9048 & 0.9061 & 0.9064 & 0.9075 & **0.9076** & 0.9069 \\ swimming-pool & 0.7115 & 0.7722 & 0.7525 & 0.7199 & 0.7670 & 0.7425 & **0.8046** & 0.8030 \\ baseball-diamond & 0.7547 & 0.8034 & 0.7573 & 0.7593 & 0.7765 & 0.7902 & 0.8077 & **0.8308** \\ roundabout & 0.7100 & 0.7371 & 0.7594 & 0.7630 & 0.7203 & 0.7712 & 0.7761 & **0.8088** \\ basketball-court & 0.7697 & 0.8823 & 0.8570 & 0.8305 & 0.8741 & **0.8996** & 0.8897 & 0.8989 \\ bridge & 0.5795 & 0.6787 & 0.6380 & 0.6562 & 0.6518 & 0.7212 & 0.6942 & **0.7294** \\ helicopter & 0.7976 & **0.8490** & 0.8422 & 0.8316 & 0.7484 & 0.8236 & 0.8432 & 0.7850 \\ all classes & 0.7549 & 0.7941 & 0.7946 & 0.7818 & 0.8065 & 0.8292 & 0.8352 & **0.8390** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: This Detailed results of mAP for each model category. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **mAP** & **mAP** & **Speed** & **FLOPs** \\  & **0.5** & **0.5:0.95** & **(ms)** & **(G)** \\ \hline YOLOX-s & 75.49 & 51.71 & **14.30** & **26.67** \\ YOLOX-m & 79.41 & 56.30 & 26.26 & 73.55 \\ YOLOX-l & 79.46 & 56.83 & 39.98 & 155.37 \\ YOLOX-x & 78.18 & 56.45 & 68.82 & 281.59 \\ DCE_YOLOX-s & 80.65 & 57.01 & 27.08 & 68.27 \\ DCE_YOLOX-m & 82.92 & 59.71 & 54.40 & 188.28 \\ DCE_YOLOX-l & 83.52 & 61.30 & 91.74 & 397.76 \\ DCE_YOLOX-x & **83.90** & **62.35** & 155.82 & 720.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison. In Figure 6 (left), there are many small objects, and the improved YOLOX before the improvement can only detect larger objects and misses other small objects, while the improved DCE_YOLOX can effectively detect the presence of small objects alone. In Figure 6 (middle), there are many small objects with a dense distribution; the original YOLOX algorithm is poor in detecting dense small objects, but the improved algorithm can detect the dense objects very well. Figure 6 (right) shows small objects with inconspicuous appearance features, which are poorly detected by the algorithm before the improvement; the improved DCE_YOLOX has a small number of missed detections, but the overall detection effect is excellent. This is due to the fact that DCE_YOLOX introduces the EcaNet effective channel attention module between the backbone network and the enhanced feature extraction network, while boosting the network image input size and adaptively calibrating the weights between channels of different feature layers, effectively fusing deep and shallow features, reducing semantic loss, and still recognizing small objects effectively. In order to compare the feature extraction ability of DCE_YOLOX and the original model, we visualized the first 32 feature images of both models separately, using the first image in Figure 6 as an example, as shown in Figure 7. We found that the improved model has better discrimination and anti-interference ability for the background, as well as better feature extraction ability for useful targets.