Great question—since you’ve already deep-dived into the core CNN-based detectors like R-CNN, Faster R-CNN, YOLO, and SSD, exploring RNN or hybrid CNN-RNN approaches is a logical and interesting next step! There are indeed several established research directions and practical solutions in this space, broken down below:

Pure RNN-Based Object Detection #

While these are less dominant than CNN-centric methods, they’re worth noting for their unique approach to modeling spatial context:

• Recurrent Region Proposal Networks: Instead of relying on CNN-generated region proposals (like Faster R-CNN’s RPN), these models use RNNs to sequentially generate and refine candidate bounding boxes. The RNN learns to iterate over potential regions, using feedback from previous predictions to narrow in on accurate proposals.
• Patch-Sequence Detection: Treating an image as a sequence of spatial patches (e.g., scanning row-by-row), RNNs process these patches in order, leveraging sequential context to detect objects—especially useful for small, densely packed objects where global spatial relationships matter.

CNN-RNN Hybrid Approaches #

These are far more prevalent, as they combine CNNs’ strength in extracting local visual features with RNNs’ ability to model sequential/spatial-temporal dependencies:

• RNN-Driven Region Refinement: Models like Faster R-CNN with LSTM Refinement use CNNs to generate initial region proposals, then feed those proposal features into an LSTM. The LSTM captures contextual relationships between proposals (e.g., "a car is likely near a road sign") to refine bounding box coordinates and reduce false positives.
• Video Object Detection (Spatial-Temporal Context): Hybrid models are a natural fit for video, where tracking temporal changes is critical:
  • ConvLSTM Detectors: Merge CNN feature extractors with ConvLSTMs (LSTMs that use convolution operations in their cells) to model how objects move and change across frames. This improves detection consistency and reduces drift in video sequences.
  • Two-Stream RNN Networks: One stream uses a CNN to extract static frame features, while the other uses an RNN to process motion information from consecutive frames. Fusing both streams gives the model a richer understanding of dynamic scenes.
• Text-Aware Object Detection: For scenes with text (e.g., street signs, product labels), CNNs pull visual features, and RNNs process OCR text sequences to add contextual cues—helping the model better classify objects that rely on text for identification.
• Attention-Guided Hybrid Models: Some architectures use RNNs to implement attention mechanisms, letting the model sequentially focus on high-priority regions of the image. For example, an LSTM might guide the CNN to zero in on areas likely containing objects, boosting both efficiency and accuracy.

Key Upsides of These Methods #

• Superior Context Modeling: RNNs excel at capturing long-range spatial or temporal dependencies that CNNs (with fixed receptive fields) might overlook.
• Iterative Refinement: Sequential proposal generation or bounding box adjustment via RNNs often leads to more precise detections.
• Dynamic Scenario Fit: Hybrid models shine in video or other dynamic environments where tracking changes over time is essential.

That said, these approaches typically come with tradeoffs—like higher computational complexity compared to pure CNN detectors—but they fill critical gaps in scenarios where context and sequence are key to accurate detection.

内容的提问来源于stack exchange，提问作者ou2105