Small Object Detection Method Based on Adaptive Spatial Parallel Convolution and Fast Multi-Scale Fusion
Abstract
:1. Introduction
- This paper proposes an adaptive feature extraction method using multi-scale receptive fields. Due to the small proportion in the image and inconspicuous features, the spatial information of small objects is always missing. The proposed method divides the input feature map equally among the channels and performs feature extraction on the separated feature channels in parallel. Additionally, the cascading relationship of multiple convolution kernels is used to achieve the effective extraction of local context information for different channels. Therefore, the features related to small objects with multi-scale spatial environmental information can be obtained by fusing the extracted information.
- This paper proposes a new feature map upsampling and multi-scale feature fusion method. This method uses both nearest-neighbor interpolation and sub-pixel convolution algorithm to map a low-resolution feature map with rich semantic information to a high-resolution space, thereby constructing a high-resolution feature map with rich semantic features. A feature map with sufficient spatial and semantic information is obtained by the fusion of the constructed feature map and a feature map with rich spatial information, thereby improving the detection ability of small objects.
- This paper designs a one-stage, real-time detection framework of small objects. The ASPConv module is proposed to extract image features from multiple channels in parallel, which effectively reduces the time complexity of feature extraction to achieve real-time small object detection. The FMF module is proposed to apply both nearest-neighbor interpolation and sub-pixel convolution to achieve a fast upsampling. The processing time of multi-scale feature map fusion is reduced by improving upsampling efficiency to ensure real-time small object detection.
2. Related Work
3. The Proposed Method
3.1. Adaptively Spatial Parallel Convolution Module
3.2. Proposed Backbone Module
3.3. Fast Multi-Scale Fusion Module
3.4. Predictor
4. Experiments
4.1. Experiment Preparation
4.1.1. Datasets and Evaluation Metrics
4.1.2. Implementation Details
4.2. Experiment Preparation
4.3. Real-Time Comparison
4.4. Ablation Study
4.5. Qualitative Results
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Layer Name | Layer Components | |
---|---|---|
Yolov5s [9] | SODNet (Proposed) | |
Focus [9] | ASPConv (Proposed) | |
CBTC1 () | ||
() | () | |
() | () | |
() | () | |
// | PANet [28] | FPN [27] |
- | FMF (Proposed) |
Methods | |||||||
---|---|---|---|---|---|---|---|
Libra RCNN [5] | 89.22 | 90.93 | 84.64 | 81.62 | 74.86 | 82.44 | 98.39 |
Grid RCNN [7] | 87.96 | 88.31 | 82.79 | 79.55 | 73.16 | 78.27 | 98.21 |
FRCNN-FPN [27] | 87.57 | 87.86 | 82.02 | 78.78 | 72.56 | 76.59 | 98.39 |
FRCNN-FPN-SM [14] | 86.22 | 87.14 | 79.60 | 76.14 | 68.59 | 74.16 | 98.28 |
FRCNN-FPN-SM [47] | 85.96 | 86.57 | 79.14 | 77.22 | 69.35 | 73.92 | 98.30 |
FCOS [48] | 96.28 | 99.23 | 96.56 | 91.67 | 84.16 | 90.34 | 99.56 |
SSD512 [4] | 93.56 | 94.55 | 90.42 | 85.54 | 76.79 | 82.80 | 99.23 |
FS-SSD512 [49] | 94.01 | 93.98 | 91.18 | 86.01 | 78.10 | 83.78 | 99.35 |
RetinaNet [13] | 92.66 | 94.52 | 88.24 | 86.52 | 82.84 | 81.95 | 99.13 |
RetinaNet-MSM [14] | 88.39 | 87.80 | 79.23 | 79.77 | 72.18 | 76.25 | 98.57 |
RetinaNet-SM [47] | 87.00 | 87.62 | 79.47 | 77.39 | 69.25 | 74.72 | 98.41 |
Scaled-YOLOv4-CSP [8] | 86.77 | 87.36 | 79.76 | 76.04 | 67.69 | 73.03 | 98.25 |
YOLOv5s [9] | 85.98 | 87.73 | 80.09 | 75.26 | 68.77 | 72.32 | 98.23 |
SODNet (Proposed) | 83.30 | 82.99 | 76.30 | 72.29 | 68.05 | 67.52 | 98.04 |
Methods | |||||||
---|---|---|---|---|---|---|---|
Libra RCNN [5] | 44.68 | 27.08 | 49.27 | 55.21 | 62.65 | 64.77 | 6.26 |
Grid RCNN [7] | 47.14 | 30.65 | 52.21 | 57.21 | 62.48 | 68.89 | 6.38 |
FRCNN-FPN [27] | 47.35 | 30.25 | 51.58 | 58.95 | 63.18 | 68.43 | 5.83 |
FRCNN-FPN-SM [14] | 51.33 | 33.91 | 55.16 | 62.58 | 66.96 | 71.55 | 6.46 |
FRCNN-FPN-SM [47] | 51.76 | 34.58 | 55.93 | 62.31 | 66.81 | 72.19 | 6.81 |
FCOS [48] | 17.90 | 2.88 | 12.95 | 31.15 | 40.54 | 41.95 | 1.50 |
SSD512 [4] | 34.00 | 13.54 | 35.16 | 48.73 | 57.14 | 61.21 | 2.52 |
FS-SSD512 [49] | 34.10 | 14.11 | 36.17 | 49.50 | 56.37 | 61.58 | 2.13 |
RetinaNet [13] | 33.53 | 12.24 | 38.79 | 47.38 | 48.26 | 61.51 | 2.28 |
RetinaNet-MSM [14] | 49.59 | 31.63 | 56.01 | 60.78 | 63.38 | 71.24 | 6.16 |
RetinaNet-SM [47] | 52.56 | 33.90 | 58.00 | 63.72 | 65.69 | 73.09 | 6.64 |
Scaled-YOLOv4-CSP [8] | 51.25 | 33.07 | 56.04 | 61.94 | 65.39 | 73.31 | 7.04 |
YOLOv5s [9] | 49.61 | 32.21 | 52.11 | 60.95 | 64.23 | 71.51 | 6.63 |
SODNet (Proposed) | 55.55 | 40.53 | 59.52 | 64.62 | 66.22 | 75.98 | 7.61 |
Methods | Small | Medium | Overall | ||||||
---|---|---|---|---|---|---|---|---|---|
FRCNN [2] +ResNet101 [41] | 80.3 | 81.6 | 80.9 | 94.5 | 94.8 | 94.7 | 89.1 | 89.7 | 89.4 |
Zhu et al. [15] | 87.0 | 82.0 | 84.4 | 94.0 | 91.0 | 92.5 | - | - | - |
Perceptual GAN [33] | 89.0 | 84.0 | 86.4 | 96.0 | 91.0 | 93.4 | - | - | - |
EFPN [45] | 87.1 | 83.6 | 85.3 | 95.2 | 95.0 | 95.1 | - | - | - |
SOS-CNN [50] | - | - | - | - | - | - | 93.0 | 90.0 | 91.5 |
Noh et al. [34] | 92.6 | 84.9 | 88.6 | 97.5 | 94.5 | 96.0 | 95.7 | 90.6 | 93.1 |
YOLOv5s [9] | 88.7 | 84.1 | 86.3 | 95.6 | 94.7 | 95.2 | 92.9 | 90.0 | 91.4 |
SODNet (Proposed) | 90.0 | 85.5 | 87.6 | 96.6 | 95.8 | 96.2 | 94.0 | 91.2 | 92.6 |
Methods | ||||
---|---|---|---|---|
R-FCN [1] | 7.0 | 17.5 | 3.9 | 4.4 |
SSD512 [4] | 9.3 | 21.4 | 6.7 | 7.1 |
RON [51] | 5.0 | 15.9 | 1.7 | 2.9 |
FRCNN [2] | 5.8 | 17.4 | 2.5 | 3.8 |
FRCNN-FPN [27] | 11.0 | 23.4 | 8.4 | 8.1 |
ClusDet [52] | 13.7 | 26.5 | 12.5 | 9.1 |
YOLOv5s [9] | 12.3 | 22.4 | 12.4 | 9.8 |
SODNet (Proposed) | 17.1 | 29.9 | 18.0 | 11.9 |
Methods | ||||||
---|---|---|---|---|---|---|
R-FCN [1] | 29.9 | 51.9 | - | 10.8 | 32.8 | 45.0 |
SSD512 [4] | 28.8 | 48.5 | 30.3 | 10.9 | 31.8 | 43.5 |
YOLOv3 [3] | 33.0 | 57.9 | 34.4 | 18.3 | 35.4 | 41.9 |
FRCNN [2] | 34.9 | 55.7 | 37.4 | 15.6 | 38.7 | 50.9 |
FRCNN-FPN [27] | 36.2 | 59.1 | 39.0 | 18.2 | 39.0 | 48.2 |
Noh et al. [34] | 34.2 | 57.2 | 36.1 | 16.2 | 35.7 | 48.1 |
YOLOv5s [9] | 35.2 | 53.9 | 37.8 | 18.8 | 39.1 | 44.0 |
SODNet (Proposed) | 36.4 | 56.2 | 39.4 | 20.1 | 40.1 | 45.7 |
Methods | Default Input Size | FPS | Uniform Input Size | FPS |
---|---|---|---|---|
FRCNN-FPN [27] | 13 | 24 | ||
FRCNN-FPN-SM [14] | 13 | 22 | ||
SSD512 [4] | 33 | 33 | ||
FS-SSD512 [49] | 30 | 30 | ||
RetinaNet-MSM [14] | 10 | 23 | ||
Scaled-YOLOv4-CSP [8] | 33 | 39 | ||
YOLOv5s [9] | 88 | 96 | ||
SODNet (Proposed) | 81 | 91 |
Methods | TinyPerson | UAVDT | ||||||
---|---|---|---|---|---|---|---|---|
Input Size | FPS | Input Size | FPS | |||||
Baseline YOLOv5s | 85.98 | 49.61 | 88 | 12.3 | 9.8 | 49 | ||
+ ASPConv | 85.46 | 51.60 | 78 | 13.4 | 10.2 | 43 | ||
+ FMF | 84.63 | 53.95 | 92 | 15.1 | 10.8 | 50 | ||
+ ASPConv + FMF | 83.30 | 55.55 | 81 | 17.1 | 11.9 | 45 |
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Qi, G.; Zhang, Y.; Wang, K.; Mazur, N.; Liu, Y.; Malaviya, D. Small Object Detection Method Based on Adaptive Spatial Parallel Convolution and Fast Multi-Scale Fusion. Remote Sens. 2022, 14, 420. https://doi.org/10.3390/rs14020420
Qi G, Zhang Y, Wang K, Mazur N, Liu Y, Malaviya D. Small Object Detection Method Based on Adaptive Spatial Parallel Convolution and Fast Multi-Scale Fusion. Remote Sensing. 2022; 14(2):420. https://doi.org/10.3390/rs14020420
Chicago/Turabian StyleQi, Guanqiu, Yuanchuan Zhang, Kunpeng Wang, Neal Mazur, Yang Liu, and Devanshi Malaviya. 2022. "Small Object Detection Method Based on Adaptive Spatial Parallel Convolution and Fast Multi-Scale Fusion" Remote Sensing 14, no. 2: 420. https://doi.org/10.3390/rs14020420
APA StyleQi, G., Zhang, Y., Wang, K., Mazur, N., Liu, Y., & Malaviya, D. (2022). Small Object Detection Method Based on Adaptive Spatial Parallel Convolution and Fast Multi-Scale Fusion. Remote Sensing, 14(2), 420. https://doi.org/10.3390/rs14020420