Incremental Learning of Infrared Vehicle Detection Method Based on SSD

Li, Hui; Jian, Yuan; Liu, Hailin; Cao, Lin; Chen, Jie; Zhang, Zhaowen

doi:10.1109/icct50939.2020.9295791

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…In order to objectively assess the effectiveness of the CSD-YOLO ship recognition network model in this paper, reasonable evaluation indices were developed. Five types of metrics were used to evaluate the performance of the algorithm: precision (P) [27], recall (R) [28], average precision map@.5 [29] and map@.5:.95 [30], and frames per second (FPS) [31]. The specific formula for each metric is as follows:…”

Section: Indicators For Model Evaluationmentioning

confidence: 99%

CSD-YOLO: A Ship Detection Algorithm Based on a Deformable Large Kernel Attention Mechanism

Wang,

Zhang,

Jiang

2024

Mathematics

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Ship detection and identification play pivotal roles in ensuring navigation safety and facilitating efficient maritime traffic management. Aiming at ship detection in complex environments, which often faces problems such as the dense occlusion of ship targets, low detection accuracy, and variable environmental conditions, in this paper, we propose a ship detection algorithm CSD-YOLO (Context guided block module, Slim-neck, Deformable large kernel attention-You Only Look Once) based on the deformable large kernel attention (D-LKA) mechanism, which was improved based on YOLOv8 to enhance its performance. This approach integrates several innovations to bolster its performance. Initially, the utilization of the Context Guided Block module (CG block) enhanced the c2f module of the backbone network, thereby augmenting the feature extraction capabilities and enabling a more precise capture of the key image information. Subsequently, the introduction of a novel neck architecture and the incorporation of the slim-neck module facilitated more effective feature fusion, thereby enhancing both the accuracy and efficiency of detection. Furthermore, the algorithm incorporates a D-LKA mechanism to dynamically adjust the convolution kernel shape and size, thereby enhancing the model’s adaptability to varying ship target shapes and sizes. To address data scarcity in complex marine environments, the experiments utilized a fused dataset comprising the SeaShips dataset and a proprietary dataset. The experimental results demonstrate that the CSD-YOLO algorithm outperformed the YOLOv8n algorithm across all model evaluation metrics. Specifically, the precision rate (precision) was 91.5%, the recall rate (recall) was 89.5%, and the mean accuracy (mAP) was 91.5%. Compared to the benchmark algorithm, the Recall was improved by 0.7% and the mAP was improved by 0.4%. These results indicate that the CSD-YOLO algorithm can effectively meet the requirements for ship target recognition and tracking in complex marine environments.

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Section: Indicators For Model Evaluationmentioning

confidence: 99%

CSD-YOLO: A Ship Detection Algorithm Based on a Deformable Large Kernel Attention Mechanism

Wang,

Zhang,

Jiang

2024

Mathematics

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“…Compared with the traditional target detection algorithm, the recognition accuracy of this method can reach 89.16%, and the average operating speed is 21FPS. In 2020, H. Li et al proposed an incremental learning infrared vehicle-detection method based on (single-hot multiBox detector (SSD) for problems related to the lack of details in infrared vehicle images [8], the difficulty in extracting feature information, and low detection accuracy. This detection method can effectively identify and locate infrared vehicles, compared with the results of infrared vehicle detection using incremental datasets and non-incremental datasets.…”

Section: Introductionmentioning

confidence: 99%

Application of Improved YOLOv5 in Aerial Photographing Infrared Vehicle Detection

et al. 2022

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Aiming to solve the problems of false detection, missed detection, and insufficient detection ability of infrared vehicle images, an infrared vehicle target detection algorithm based on the improved YOLOv5 is proposed. The article analyzes the image characteristics of infrared vehicle detection, and then discusses the improved YOLOv5 algorithm in detail. The algorithm uses the DenseBlock module to increase the ability of shallow feature extraction. The Ghost convolution layer is used to replace the ordinary convolution layer, which increases the redundant feature graph based on linear calculation, improves the network feature extraction ability, and increases the amount of information from the original image. The detection accuracy of the whole network is enhanced by adding a channel attention mechanism and modifying loss function. Finally, the improved performance and comprehensive improved performance of each module are compared with common algorithms. Experimental results show that the detection accuracy of the DenseBlock and EIOU module added alone are improved by 2.5% and 3% compared with the original YOLOv5 algorithm, respectively, and the addition of the Ghost convolution module and SE module alone does not increase significantly. By using the EIOU module as the loss function, the three modules of DenseBlock, Ghost convolution and SE Layer are added to the YOLOv5 algorithm for comparative analysis, of which the combination of DenseBlock and Ghost convolution has the best effect. When adding three modules at the same time, the mAP fluctuation is smaller, which can reach 73.1%, which is 4.6% higher than the original YOLOv5 algorithm.

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“…In recent years, deep learning technology has rapidly developed, and various network models have been widely used in target detection of infrared thermal images. [17][18][19] For example, Faster-RCNN, 20 SSD (Single Shot multi-box detector), 21 YOLO v3, 22,23 and YOLO V4 24 were used to detect vehicles in infrared thermal images and had achieved good results. To improve the efficiency of vehicle detection, Cai et al 25 and Wang et al 26 adopted grayscale segmentation and visual saliency method to extract the vehicle ROI.…”

Section: Introductionmentioning

confidence: 99%

Vehicle detection in severe weather based on pseudo-visual search and HOG–LBP feature fusion

Wang

Zhan

Duan

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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Vehicle detection in severe weather has always been a difficult task in the environmental perception of intelligent vehicles. This paper proposes a vehicle detection method based on pseudo-visual search and the histogram of oriented gradients (HOG)–local binary pattern (LBP) feature fusion. Using radar detection information, this method can directly extract the region of interest (ROI) of vehicles from infrared images by imitating human vision. Unlike traditional methods, the pseudo-visual search mechanism is independent of complex image processing and environmental interferences, thereby significantly improving the speed and accuracy of ROI extraction. More notably, the ROI extraction process based on pseudo-visual search can reduce image processing by 40%–80%, with an ROI extraction time of only 4 ms, which is far lower than the traditional algorithms. In addition, we used the HOG–LBP fusion feature to train the vehicle classifier, which improves the extraction ability of local and global features of vehicles. The HOG–LBP fusion feature can improve vehicle detection accuracy by 6%–9%, compared to a single feature. Experimental results show that the accuracy of vehicle detection is 92.7%, and the detection speed is 31 fps, which validates the feasibility of the proposed method and effectively improve the vehicle detection performance in severe weather

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Incremental Learning of Infrared Vehicle Detection Method Based on SSD

Cited by 6 publications

References 10 publications

CSD-YOLO: A Ship Detection Algorithm Based on a Deformable Large Kernel Attention Mechanism

CSD-YOLO: A Ship Detection Algorithm Based on a Deformable Large Kernel Attention Mechanism

Application of Improved YOLOv5 in Aerial Photographing Infrared Vehicle Detection

Vehicle detection in severe weather based on pseudo-visual search and HOG–LBP feature fusion

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