SlimYOLOv4: lightweight object detector based on YOLOv4

Ding, Peng; Qian, Huaming; Chu, Shuai

doi:10.1007/s11554-022-01201-7

Cited by 27 publications

(6 citation statements)

References 32 publications

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“…Since their inception, UAVs have mainly used in the military field [4]. With the development and application of UAV technology, UAV technology has been widely used in crop protection, photography, disaster relief, and other civilian fields.…”

Section: Introductionmentioning

confidence: 99%

Detection of Pine Wilt Nematode from Drone Images Using UAV

Sun

Ibrayim

Hamdulla

2022

Sensors

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Pine wilt nematode disease is a devastating forest disease that spreads rapidly. Using drone remote sensing to monitor pine wilt nematode trees promptly is an effective way to control the spread of pine wilt nematode disease. In this study, the YOLOv4 algorithm was used to automatically identify abnormally discolored wilt from pine wilt nematode disease on UAV remote sensing images. Because the network structure of YOLOv4 is too complex, although the detection accuracy is high, the detection speed is relatively low. To solve this problem, the lightweight deep learning network MobileNetv2 is used to optimize the backbone feature extraction network. Furthermore, the YOLOv4 algorithm was improved by improving the backbone network part, adding CBAM attention, and adding the Inceptionv2 structure to reduce the number of model parameters and improve the accuracy and efficiency of identification. The speed and accuracy of the Faster R-CNN, YOLOv4, SSD, YOLOv5, and the improved MobileNetv2-YOLOv4 algorithm were compared, and the detection effects of the Faster R-CNN, YOLOv4, SSD, YOLOv5 and the improved MobileNetv2-YOLOv4 algorithm on trees with pine wilt nematode were analyzed. The experimental results show that the average precision of the improved MobileNetv2-YOLOv4 algorithm is 86.85%, the training time of each iteration cycle is 156 s, the parameter size is 39.23 MB, and the test time of a single image is 15 ms, which is better than Faster R-CNN, YOLOv4, and SSD, but comparable to YOLOv5. Compared with the advantages and disadvantages, comprehensively comparing these four indicators, the improved algorithm has a more balanced performance in the detection speed, the parameter size, and the average precision. The F1 score of the improved algorithm (95.60%) was higher than that of Faster R-CNN (90.80%), YOLOv4 (94.56%), and SSD (92.14%), which met the monitoring requirements of pine wilt nematode trees. Faster R-CNN and SSD pine-wilt-nematode tree detection models are not ideal in practical applications. Compared with the YOLOv4 pine-wilt-nematode tree detection model, the improved MobileNetv2-YOLOv4 algorithm satisfies the condition of maintaining a lower model parameter quantity to obtain higher detection accuracy; therefore, it is more suitable for practical application scenarios of embedded devices. It can be used for the rapid detection of pine wilt nematode diseased trees.

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Section: Introductionmentioning

confidence: 99%

Detection of Pine Wilt Nematode from Drone Images Using UAV

Sun

Ibrayim

Hamdulla

2022

Sensors

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“…These convolutional layers serve to extract a comprehensive representation of input features while pooling layers aid in reducing the feature map size, thereby decreasing computational demands and enhancing representation robustness. Nevertheless, these blocks still entail considerable computational complexity, as has been discussed in previous studies [46] and [47]. Nevertheless, the challenge becomes more pronounced when dealing with small-sized objects, such as pedestrians in this context.…”

Section: ) Modified Backbone Structurementioning

confidence: 90%

Enhancing Indoor Robot Pedestrian Detection Using Improved PIXOR Backbone and Gaussian Heatmap Regression in 3D LiDAR Point Clouds

Nguyen,

Hoang,

Nguyen

et al. 2024

IEEE Access

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Accurate and robust pedestrian detection is fundamental for indoor robotic systems to navigate safely and seamlessly alongside humans in spatially constrained, unpredictable indoor environments. This paper presents a novel method, IRBGHR-PIXOR, a detection framework specifically engineered for pedestrian perception in indoor mobile robots. This novel approach employs an enhanced adaptation of the cutting-edge PIXOR model, integrating two pivotal augmentations: a remodeled convolutional backbone leveraging Inverted Residual Blocks (IRB) in unison with Gaussian Heatmap Regression (GHR), as well as a Modified Focal Loss (MFL) function to tackle data imbalance issues. The IRB component notably bolsters the network's aptitude for processing intricate spatial representations generated from sparse 3D LiDAR scans. Meanwhile, integrating GHR further elevates accuracy by enabling precise localization of pedestrian subjects. This is achieved by modeling the probability distribution and predicting the central location of individuals in the point cloud data. Extensively evaluated on the large-scale JRDB dataset comprising intense scans from 16-beam Velodyne LiDAR sensors, IRBGHR-PIXOR accomplishes exceptional results, attaining 97.17% Average Precision (AP) at the 0.5 IOU threshold. Notably, this level of accuracy is achieved without significantly increasing model complexity. By enhancing algorithms to overcome challenges in confined indoor environments, this research paves the way for safe and effective deployment of autonomous technologies once encumbered by perceptual limitations in human-centered spaces. Nonetheless, evaluating performance in diverse edge cases and integration with complementary sensory cues promise continued progress. The developments contribute towards the vital capacity of reliable dynamic perception for next-generation robotic systems coexisting in human-centric environments.

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“…In order to optimize the feature fusion network, it introduces a lightweight feature-preserving and refinement module. SlimYOLOv4 [32] takes MobileNetv2 as the feature extraction network. Meanwhile, conventional convolution is replaced by more suitable depthwise overparameterized depthwise convolution, which improves network performance while reducing the computation.…”

Section: B Lightweight Networkmentioning

confidence: 99%

P2RNet: Fast Maritime Object Detection From Key Points to Region Proposals in Large-Scale Remote Sensing Images

Chen,

Wang,

Zhang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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SlimYOLOv4: lightweight object detector based on YOLOv4

Cited by 27 publications

References 32 publications

Detection of Pine Wilt Nematode from Drone Images Using UAV

Detection of Pine Wilt Nematode from Drone Images Using UAV

Enhancing Indoor Robot Pedestrian Detection Using Improved PIXOR Backbone and Gaussian Heatmap Regression in 3D LiDAR Point Clouds

P2RNet: Fast Maritime Object Detection From Key Points to Region Proposals in Large-Scale Remote Sensing Images

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