In a real-world traffic scenario, varying-scale objects are usually distributed in a cluttered background, which poses great challenges to accurate detection. Although current Mamba-based methods can efficiently model long-range dependencies, they still struggle to capture small objects with abundant local details, which hinders joint modeling of local structures and global semantics. Moreover, state-space models exhibit limited hierarchical feature representation and weak cross-scale interaction due to flat sequential modeling and insufficient spatial inductive biases, leading to sub-optimal performance in complex scenes. To address these issues, we propose a Mamba with Deformable Dilated Convolutions Network (MDDCNet) for accurate traffic object detection in this study. In MDDCNet, a well-designed hybrid backbone with successive Multi-Scale Deformable Dilated Convolution (MSDDC) blocks and Mamba blocks enables hierarchical feature representation from local details to global semantics. Meanwhile, a Channel-Enhanced Feed-Forward Network (CE-FFN) is further devised to overcome the limited channel interaction capability of conventional feed-forward networks, whilst a Mamba-based Attention-Aggregating Feature Pyramid Network (A^2FPN) is constructed to achieve enhanced multi-scale feature fusion and interaction. Extensive experimental results on public benchmark and real-world datasets demonstrate the superiority of our method over various advanced detectors. The code is available at https://github.com/Bettermea/MDDCNet.
Figures & tables
Figure 1: The model architecture of our proposed MDDCNet which comprises Hybrid Backbone (a), Attention-Aggregating Feature Pyramid Network ( A2 FPN) (b) and Detection Head (c).
Figure 2: Illustration of the hybrid backbone network consisting of successive MSDDC and Mamba blocks.
Model variants
MDDCNet-N
MDDCNet-T
MDDCNet-B
Embed. Dim.
[16, 32, 64, 128]
[32, 64, 128, 256]
[64, 128, 256, 512]
Stage 1 (H/4 × W/4)
MSDDC × 3
MSDDC × 3
MSDDC × 3
Stage 2 (H/8 × W/8)
MSDDC × 3
MSDDC × 3
MSDDC × 3
Stage 3 (H/16 × W/16)
Mamba × 9
Mamba × 9
Mamba × 12
Stage 4 (H/32 × W/32)
Mamba × 3
Mamba × 3
Mamba × 3
Params (M)
4.8
6.6
18.0
Table 1: Configurations of different model variants in our MDDCNet family.
Figure 3: Comparison of our proposed CE-FFN (c) and the vanilla FFN (a) and FFN with CA-Block (b).
Figure 4: Our proposed Contextual-Spatial-Channel Attention (CSCA) synergy module. Comprising three complementary attention branches, it leverages an attention-aggregating mechanism for comprehensively strengthening feature discriminative power, which significantly benefits multi-scale feature fusion and interaction.
Figure 5: Statistics of KITTI dataset in traffic object categories (a) and scale distributions (b).
Figure 6: Statistics of our RTOD dataset in traffic object categories (a) and scale distributions (b).
Figure 7: Example images in RTOD dataset.
Detectors
P(%)
R(%)
mAP@50(%)
mAP@50-95(%)
Params(M)
FLOPs(G)
FPS
YOLOv3t
87.7
75.3
82.1
58.3
12.1
18.9
67.52
YOLOv5n
91.3
77.8
88.5
63.3
2.5
7.2
80.64
YOLOv6s
89.8
76.4
85.3
60.6
4.2
11.9
80.65
YOLOv8n
90.5
80.9
89.5
65.1
3.0
8.2
85.32
YOLOv8s
93.1
87.6
93.3
73.0
11.1
28.5
66.67
YOLOv10n
83.7
79.1
85.9
62.5
2.7
8.4
90.50
Table 2: Comprehensive comparison on the KITTI Dataset. The results of our method are highlighted in bold.
Detectors
Car
Van
Truck
Tram
Pedestrian
Cyclist
Misc
YOLOv3t
88.7
86.9
97.3
87.8
68.8
65.8
79.7
YOLOv5n
95.2
91.5
96.9
92.2
76.5
80.7
86.4
YOLOv6s
94.9
87.9
95.6
91.0
76.7
73.9
77.1
YOLOv8n
95.7
93.1
95.8
92.0
79.3
78.6
92.0
YOLOv8s
97.3
96.8
98.8
94.3
84.8
85.8
95.2
YOLOv10n
94.7
90.3
96.2
91.0
73.0
77.0
79.2
Table 3: Comparison of different detectors across various categories on the KITTI dataset (mAP@50%).
Figure 8: PR curves of YOLOv8n (a), YOLOv10n (b), YOLOv11n (c), YOLOv12n (d), YOLOv13n (e) and our MDDCNet-T (f) on the KITTI dataset.
Detectors
P(%)
R(%)
mAP@50(%)
mAP@50-95(%)
FPS
YOLOv5n
87.5
76.1
82.1
57.6
46.18
YOLOv6s
84.3
73.3
79.2
55.2
48.82
YOLOv8n
89.9
79.1
84.3
60.6
55.56
YOLOv9t
83.9
77.3
81.2
57.9
51.28
YOLOv11n
87.1
78.9
84.3
60.1
55.25
YOLOv12n
89.2
77.4
81.7
58.0
52.36
Table 4: Comprehensive comparison of different models on the RTOD Dataset.
Figure 9: PR curves of YOLOv8n (a), YOLOv13n (b), MambaYOLO-T (c), and our MDDCNet-T (d) on the RTOD dataset.
Detectors
Car
Van
Bus
Truck
Person
Cycle
Plate
Traffic light
Traffic sign
Others
YOLOv5n
95.7
87.2
96.9
84.2
71.0
89.1
10.0
98.3
94.5
94.1
YOLOv6s
94.9
79.9
97.8
78.5
67.1
89.8
2.1
97.7
94.1
89.8
YOLOv8n
95.9
87.1
99.4
87.6
72.7
87.5
21.4
97.7
94.1
99.5
YOLOv9t
95.6
87.1
98.6
82.6
67.4
89.9
8.6
97.9
88.3
95.9
YOLOv11n
95.6
86.2
96.8
88.7
71.9
91.1
23.3
97.9
93.5
97.4
YOLOv12n
95.4
86.9
97.7
86.0
70.8
90.0
0.3
98.0
94.9
96.7
Table 5: Comparison of different detectors across various categories on the RTOD dataset (mAP@50%).
Backbone architectures
mAP@50(%)
mAP@50-95(%)
Params(M)
FLOPs(G)
[MSDDC,MSDDC,MSDDC,MSDDC]
91.6
68.4
7.4
13.4
[Mamba,Mamba,Mamba,Mamba]
91.5
69.7
4.9
11.1
[Mamba,Mamba,MSDDC,MSDDC]
91.7
69.1
7.3
12.9
[MSDDC,MSDDC,Mamba,Mamba]
92.1
69.7
5.0
11.6
Table 6: Ablation experiments on backbone structures.
Setting
P(%)
R(%)
mAP@50(%)
mAP@50-95(%)
Params(M)
FLOPs(G)
DR=1,2,3
92.2
84.7
91.6
69.9
5.0
11.6
DR=1,3,5
91.5
84.8
91.9
68.6
5.0
11.6
DR=1,2,4
93.4
83.9
92.1
69.7
5.0
11.6
Table 7: Ablation experiments on the Dilation Rates (DR) in our MSDDC module.
Attention
P(%)
R(%)
mAP@50(%)
mAP@50-95(%)
Params(M)
FLOPs(G)
Concat
92.2
84.7
91.5
69.7
4.9
11.1
MLCA-only
91.6
83.4
91.2
69.4
5.0
11.3
RCSSC
92.8
86.4
91.8
71.3
5.3
12.0
CSCA
93.9
85.4
92.5
71.1
5.5
11.6
Table 8: Ablation experiments on different attention mechanisms.
Methods
P(%)
R(%)
mAP@50(%)
mAP@50-95(%)
Params(M)
FLOPs(G)
Vanilla FFN
92.2
84.7
91.5
69.7
4.9
11.1
CA Block
92.0
84.2
91.7
69.9
5.3
11.5
Residual CA Block
91.0
85.7
91.8
69.4
5.3
11.6
Gated CA Block
91.2
85.3
92.0
69.6
5.5
11.7
CE-FFN
94.2
84.0
92.3
70.0
5.7
11.8
Table 9: Ablation experiments on different FFN structures.
Modules
P(%)
R(%)
mAP@50(%)
mAP@50-95(%)
Params(M)
FLOPs(G)
CE-FFN
MSDDC
CSCA
92.5
84.3
91.5
69.7
4.9
11.1
✓
94.2
84.0
92.3
70.0
5.7
11.8
✓
93.4
83.9
92.1
69.7
5.0
11.6
✓
93.9
85.4
92.5
71.1
5.5
11.6
✓
✓
92.2
85.8
92.8
69.3
6.0
12.2
Table 10: Comprehensive ablation experiments of our MDDCNet model.
Figure 10: Qualitative comparison of YOLOv13n and our MDDCNet on the KITTI dataset. Our MDDCNet demonstrates superior performance with fewer missed detections.
Figure 11: Qualitative comparison of YOLOv13n and our MDDCNet on the RTOD dataset. It can be observed that our method outperforms YOLOv13n by achieving more accurate results with fewer missed targets and lower false positive rates.
Intelligent Software Research Center, Institute of Software, CAS · University of Chinese Academy of Sciences · State Key Lab of Processors, Institute of Computing Technology, CAS +1