Mask-guided SSD for small-object detection

Sun, Chuen-Tsai; Ai, Yibo; Wang, Sheng; Zhang, Weidong

doi:10.1007/s10489-020-01949-0

Cited by 57 publications

(23 citation statements)

References 44 publications

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“…To further verify the effectiveness of our algorithm network, our algorithm was tested on another pedestrian detection dataset Caltech. Based on the experimental results obtained on the Caltech dataset, we compared the proposed algorithm with mainstream algorithms, such as RPN + BF [2], Faster R-CNN [4], GDFL [32], SDS-RCNN [35], AMS-Net [30], MGAN [12], ALFNet [29], HyperLearner [36], WiderPerson [31], DR-CNN [28], CSP [3], Couple [37] and Mask-SSD [38], is shown in Fig. 11.…”

Section: Experimental Results and Comparisonmentioning

confidence: 99%

MAFA-net: pedestrian detection network based on multi-scale attention feature aggregation

Xia

Wan

et al. 2021

Appl Intell

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With pedestrian detection algorithms, balancing the trade-off between accuracy and speed remains challenging. Following the central point-based one-stage object detection paradigm, a pedestrian detection algorithm based on multi-scale attention feature aggregation (MAFA) is proposed to improve accuracy while considering real-time performance. We refer to the proposed algorithm as MAFA-Net. Through the design of deep dilate blocks, deeper features are extracted. Pedestrian attention blocks are added to mine more relevant information between features from the perspective of spatial and passage-wise dimensions, and pedestrian features are enhanced. Feature aggregation modules are used to fuse different scale features, and combine the rich high-level semantic features with the accurate location features of the low-level features. Experiments were conducted on two challenging pedestrian detection datasets, i.e., CityPersons and Caltech, using MR −2 as the evaluation index. For Caltech, MR −2 is 4.58% under reasonable conditions. For CityPersons, MR −2 is 11.47% and 10.05% under reasonable and partial occlusion conditions, which is 0.43% and 1.35% better than the suboptimal comparison detection method. The results demonstrate that a good performance is obtained, and the effectiveness and feasibility of the algorithm are verified.

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Section: Experimental Results and Comparisonmentioning

confidence: 99%

MAFA-net: pedestrian detection network based on multi-scale attention feature aggregation

Xia

Wan

et al. 2021

Appl Intell

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show abstract

“…Another study [39] uses contextual information besides the multi-scale representation obtained from SSD model. Pan et.…”

Section: Small Object Detectionmentioning

confidence: 99%

Focus-and-Detect: A small object detection framework for aerial images

Koyun

Keser

Akkaya

et al. 2022

Signal Processing: Image Communication

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“…It treats a composite object as a group of parts and incorporates part information into context information to improve composite object detection. Sun et al [ 27 ] constructed a Mask-SSD network, increasing the SSD performance for detecting target objects of small size by enhancing detection features with contextual information and introducing a segmentation mask to eliminate background regions. Wu et al [ 28 ] have proposed a new pipeline for salient end-to-end instance segmentation (SIS) that predicts a class-agnostic mask for each detected salient instance.…”

Section: Related Workmentioning

confidence: 99%

An Embeddable Algorithm for Automatic Garbage Detection Based on Complex Marine Environment

Deng

Ergu

Liu

et al. 2021

Sensors

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With the continuous development of artificial intelligence, embedding object detection algorithms into autonomous underwater detectors for marine garbage cleanup has become an emerging application area. Considering the complexity of the marine environment and the low resolution of the images taken by underwater detectors, this paper proposes an improved algorithm based on Mask R-CNN, with the aim of achieving high accuracy marine garbage detection and instance segmentation. First, the idea of dilated convolution is introduced in the Feature Pyramid Network to enhance feature extraction ability for small objects. Secondly, the spatial-channel attention mechanism is used to make features learn adaptively. It can effectively focus attention on detection objects. Third, the re-scoring branch is added to improve the accuracy of instance segmentation by scoring the predicted masks based on the method of Generalized Intersection over Union. Finally, we train the proposed algorithm in this paper on the Transcan dataset, evaluating its effectiveness by various metrics and comparing it with existing algorithms. The experimental results show that compared to the baseline provided by the Transcan dataset, the algorithm in this paper improves the mAP indexes on the two tasks of garbage detection and instance segmentation by 9.6 and 5.0, respectively, which significantly improves the algorithm performance. Thus, it can be better applied in the marine environment and achieve high precision object detection and instance segmentation.

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Mask-guided SSD for small-object detection

Cited by 57 publications

References 44 publications

MAFA-net: pedestrian detection network based on multi-scale attention feature aggregation

MAFA-net: pedestrian detection network based on multi-scale attention feature aggregation

Focus-and-Detect: A small object detection framework for aerial images

An Embeddable Algorithm for Automatic Garbage Detection Based on Complex Marine Environment

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