SSD7-FFAM: A Real-Time Object Detection Network Friendly to Embedded Devices from Scratch

Abstract

The high requirements for computing and memory are the biggest challenges in deploying existing object detection networks to embedded devices. Living lightweight object detectors directly use lightweight neural network architectures such as MobileNet or ShuffleNet pre-trained on large-scale classification datasets, which results in poor network structure flexibility and is not suitable for some specific scenarios. In this paper, we propose a lightweight object detection network Single-Shot MultiBox Detector (SSD)7-Feature Fusion and Attention Mechanism (FFAM), which saves storage space and reduces the amount of calculation by reducing the number of convolutional layers. We offer a novel Feature Fusion and Attention Mechanism (FFAM) method to improve detection accuracy. Firstly, the FFAM method fuses high-level semantic information-rich feature maps with low-level feature maps to improve small objects’ detection accuracy. The lightweight attention mechanism cascaded by channels and spatial attention modules is employed to enhance the target’s contextual information and guide the network to focus on its easy-to-recognize features. The SSD7-FFAM achieves 83.7% mean Average Precision (mAP), 1.66 MB parameters, and 0.033 s average running time on the NWPU VHR-10 dataset. The results indicate that the proposed SSD7-FFAM is more suitable for deployment to embedded devices for real-time object detection.

Hybrid pooling and channel attention‐based single‐shot detector for printed circuit board defect detection

“…Unlike the convolutional operator, the weights utilized in the weighted average operation of self‐attention are produced dynamically via a similarity function between hidden units [13]. Recently, many works have incorporated attention into CNNs to improve performance for classification and detection [14]. Two famous modules are spatial attention and CA.…”

A Fast and Accurate Few-Shot Detector for Objects with Fewer Pixels in Drone Image

“…RefineDet [28] introduced the anchor refinement module (ARM) on the basis of one-stage model to filter out negative anchors to reduce search space for the classifier and coarsely adjust the locations of anchors for the subsequent regressor. Reference [29] proposed a seven-layer convolutional lightweight real-time detector SSD7-FFAM for embedded devices, which applied a novel feature fusion and attention mechanism to alleviate the impact of decreasing the number of convolutional layers, and it performed well on NWPU VHR-10. Nevertheless, since the accuracy of these one-stage model trails that of two stage methods, improving the detection accuracy of one-stage model is still an enormous challenge in objection detection.…”