An Information-Reserved and Deviation-Controllable Binary Neural Network for Object Detection
Abstract
:1. Introduction
- We propose a method with multiple entropy constraints to improve the performance of information retention networks, including information entropy and relative entropy.
- We propose a dynamic scaling factor to control the deviation between the binary network and the full-precision network, so that the performance of the binary network is closer to the full-precision network.
- We simultaneously optimize the binary network from both the perspectives of information retention and deviation control for effective object detection.
- We evaluate the IR-DC Net method on PASCAL VOC, COCO, KITTI, and VisDrone datasets to enable a comprehensive comparison with the state-of-the-art binary networks in object detection.
2. Related work
2.1. Binary Convolutional Neural Network
2.2. Object Detection
3. Preliminaries
4. Proposed Method
4.1. Information Retention with Multi-Entropy
4.2. Deviation Control with Dynamic Scale Factor
5. Experiments
5.1. Datasets
5.2. Implementation Details
5.3. Ablation Study
5.3.1. Effect of Information Retention
5.3.2. Effect of KL-Divergency Loss
5.3.3. Effect of Dynamic Scale Factor
6. Discussion
6.1. The Results on PASCAL VOC
6.2. The Results on COCO2014
6.3. The Results on KITTI
6.4. The Results on VisDrone2019
6.5. Visualization Results
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Year | Method | Main Idea | Authors |
---|---|---|---|
2016 | BNN | It quantifies the activation function and weight as +1 and −1. | COURBARIAUX M et al. |
2016 | XNOR-Net | On the basis of binary weight and activation, it introduces scale factor a to reduce the error. | RASTEGARI M et al. |
2018 | Bi-Real Net | It provides a user-defined ApprovSign function to replace the sign function for gradient operation in backpropagation. | Z. C. LIU et al. |
2020 | IR-Net | It keeps the information in the network from both the forward propagation and the back propagation. | H. T. QIN et al. |
2020 | ReActNet | It improves the sign and PReLU functions and increases the learnable coefficients to reduce the distribution error. | Z. C. LIU et al. |
2020 | BiDet | It introduces the information bottleneck principle to reduce false positives by eliminating redundancy. | Z. W. WANG et al. |
2021 | BTM | It proposes a binary training mechanism based on feature distribution and a multi-stage knowledge distillation strategy. | X. R. JIANG et al. |
2022 | GroupNet | It divides the network into several groups and proposes binary parallel convolution, which can embed multi-scale information into BNN. | B. H. ZHUANG et al. |
2022 | CABNN | It introduces a self-adjusting activation distribution of RPReLU with learnable coefficients for feature screening. | W. P. JING et al. |
Year | Method | Main Idea | Authors |
---|---|---|---|
2014 | R-CNN | It applies CNN to bottom-up candidate regions to locate and segment objects. | Ross Girshick et al. |
2015 | Fast R-CNN | It proposes RoI Pooling layer to unify features, which can train detectors and bounding box regressors simultaneously. | Ross Girshick |
2015 | Faster R-CNN | It uses Region Proposal Network instead of Selective Search, and it is the first near real-time and end-to-end deep learning detector. | S. H. REN et al. |
2016 | YOLO | It applies a single neural network to the whole image and transforms the object detection problem into a regression problem. It is the first single stage detector. | Joseph Redmon et al. |
2016 | SSD | It introduces multi-reference and multi-resolution technology to improve the detection accuracy, including small objects. | W. LIU et al. |
2017 | FPN | It develops a top-down architecture with lateral connections for difficult object-locating problems. | Tsung-Yi Lin et al. |
2017 | RetinaNet | It solves the imbalance between foreground and background levels by reconstructing the standard cross entropy loss. | Tsung-Yi Lin et al. |
Dataset | Types | Training Set | Validation Set | Evaluation Standard |
---|---|---|---|---|
PASCAL VOC | 20 | 16k | 5k | mAP |
COCO | 80 | 83k | 40k | AP@0.5 |
KITTI | 3 | 3.7k | 3.8k | mAP |
VisDrone | 4 | 16k | 9k | mAP |
Methods | Type | Car (IoU = 0.5) | Car (IoU = 0.7) | ||||
---|---|---|---|---|---|---|---|
Easy | Moderate | Hard | Easy | Moderate | Hard | ||
SE-SSD | FP | 96.69 | 95.6 | 90.53 | 96.65 | 93.27 | 88.14 |
SE-SSD* | 1/1 | 56.69 | 46.87 | 44.68 | 53.07 | 43.43 | 41.34 |
BNN | 1/1 | 63.3 | 50.73 | 32.23 | 58.27 | 43.24 | 27.67 |
XNOR-Net | 1/1 | 65.01 | 51.26 | 35.73 | 59.07 | 47.06 | 32.61 |
BiDet | 1/1 | 64.1 | 56.36 | 40.56 | 59.76 | 44.06 | 29.07 |
AutoBiDet | 1/1 | 65.57 | 57.88 | 41.16 | 61.38 | 46.63 | 32.2 |
IR-DC | 1/1 | 69.8 | 59.09 | 49.45 | 61.66 | 48.63 | 41.69 |
Methods | Type | Cyclist (IoU = 0.5) | ||
---|---|---|---|---|
Easy | Moderate | Hard | ||
SE-SSD | FP | 88.99 | 78.71 | 72.03 |
SE-SSD* | 1/1 | 61.42 | 45.41 | 38.26 |
BNN | 1/1 | 49.98 | 28.46 | 22.41 |
Xnor-Net | 1/1 | 59.09 | 35.22 | 26.54 |
BiDet | 1/1 | 52.47 | 42.86 | 34.97 |
AutoBiDet | 1/1 | 62.44 | 47.15 | 40.87 |
IR-DC | 1/1 | 61.53 | 48.4 | 39.43 |
Methods | Type | Pedestrain (IoU = 0.3) | ||
---|---|---|---|---|
Easy | Moderate | Hard | ||
SE-SSD | FP | 72.33 | 60.51 | 56.28 |
SE-SSD* | 1/1 | 46.69 | 35.87 | 31.68 |
BNN | 1/1 | 34.35 | 23.92 | 20.43 |
Xnor-Net | 1/1 | 46.38 | 30.39 | 26.17 |
BiDet | 1/1 | 41.38 | 31.97 | 30.15 |
AutoBiDet | 1/1 | 49.55 | 33.5 | 27.12 |
IR-DC | 1/1 | 51.58 | 35.79 | 31.14 |
IR | KL-D | DSF | Car | |||||
---|---|---|---|---|---|---|---|---|
IoU = 0.5 | IoU = 0.7 | |||||||
Easy | Moderate | Hard | Easy | Moderate | Hard | |||
✓ | 53.17 | 35.81 | 33.01 | 35.8 | 24.05 | 19.97 | ||
✓ | 53.19 | 33.49 | 30.73 | 34.82 | 25.31 | 18.32 | ||
✓ | 53.22 | 41.38 | 33.65 | 36.14 | 25.75 | 19.73 | ||
✓ | ✓ | 63.2 | 53.7 | 40.68 | 45.77 | 33.29 | 27.7 | |
✓ | ✓ | 63.07 | 54.15 | 41.17 | 45.15 | 35.57 | 28.59 | |
✓ | ✓ | 60.13 | 51.15 | 43.01 | 47.5 | 36.64 | 31.81 | |
✓ | ✓ | ✓ | 69.8 | 59.09 | 49.45 | 61.66 | 48.63 | 41.69 |
Baseline | Bit-Width | Params | mAP | |
---|---|---|---|---|
VGG16 | BNN | 1/1 | 22.06 | 42 |
XNOR-Net | 1/1 | 22.16 | 50.2 | |
BiDet | 1/1 | 22.06 | 52.4 | |
AutoBiDet | 1/1 | 22.06 | 53.5 | |
IR-DC | 1/1 | 22.16 | 55.3 | |
MobileNet | 32/32 | 100.28 | 72.4 | |
ResNet20 | BNN | 1/1 | 2.38 | 35.6 |
XNOR-Net | 1/1 | 2.48 | 48.4 | |
BiDet | 1/1 | 2.38 | 50 | |
AutoBiDet | 1/1 | 2.38 | 50.7 | |
IR-DC | 1/1 | 2.48 | 51.2 | |
IR-DC | 4/1 | 2.68 | 58.1 |
Methods | AP | |||||
---|---|---|---|---|---|---|
S | M | L | IoU = 0.3 | IoU = 0.5 | ||
VGG16 | BNN | 2.4 | 10 | 9.9 | 28.1 | 15.9 |
XNOR-Net | 2.6 | 8.3 | 13.3 | 33.4 | 19.5 | |
BiDet | 5.1 | 14.3 | 20.5 | 46.1 | 28.3 | |
AutoBiDet | 5.6 | 16.1 | 21.9 | 48.4 | 30.3 | |
IR-DC | 6.2 | 17.6 | 22 | 49.7 | 32 | |
ResNet20 | BNN | 2 | 8.5 | 9.3 | 26 | 14.3 |
XNOR-Net | 2.7 | 11.8 | 15.9 | 34.4 | 21.6 | |
BiDet | 4.9 | 16.7 | 25.4 | 47.6 | 31 | |
AutoBiDet | 5 | 17.2 | 25.9 | 48.4 | 31.5 | |
IR-DC | 5.3 | 17.3 | 25.1 | 49.9 | 32 |
Method | Pedestrian | Bicycle | Car | Van | Tricycle | Bus | Motor | mAP |
---|---|---|---|---|---|---|---|---|
BNN | 12.08 | 9.84 | 48.08 | 34.65 | 15.47 | 42.95 | 13.1 | 20.05 |
XNOR-Net | 13.7 | 8.66 | 51.71 | 34.75 | 13.11 | 44.94 | 15.98 | 20.25 |
BiDet | 15.46 | 10.02 | 52.33 | 34.63 | 14.83 | 50.78 | 20.69 | 22.05 |
AutoBiDet | 18.17 | 14.73 | 57.38 | 34.81 | 18.64 | 50.22 | 19.67 | 26.23 |
IR-DC | 19.18 | 14.29 | 59.19 | 34.37 | 15.62 | 52.29 | 20.03 | 26.2 |
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Zhu, G.; Fei, H.; Hong, J.; Luo, Y.; Long, J. An Information-Reserved and Deviation-Controllable Binary Neural Network for Object Detection. Mathematics 2023, 11, 62. https://doi.org/10.3390/math11010062
Zhu G, Fei H, Hong J, Luo Y, Long J. An Information-Reserved and Deviation-Controllable Binary Neural Network for Object Detection. Mathematics. 2023; 11(1):62. https://doi.org/10.3390/math11010062
Chicago/Turabian StyleZhu, Ganlin, Hongxiao Fei, Junkun Hong, Yueyi Luo, and Jun Long. 2023. "An Information-Reserved and Deviation-Controllable Binary Neural Network for Object Detection" Mathematics 11, no. 1: 62. https://doi.org/10.3390/math11010062
APA StyleZhu, G., Fei, H., Hong, J., Luo, Y., & Long, J. (2023). An Information-Reserved and Deviation-Controllable Binary Neural Network for Object Detection. Mathematics, 11(1), 62. https://doi.org/10.3390/math11010062