## 2 Model Construction
### Backbone Network-RevCol  
Mainstream visual models predominantly concentrate on representational and perceptual capabilities, particularly in the context of supervised training. These models tend to compress or discard features during layer transfer, retaining only the most pertinent information aligned with supervisory input. However, this approach often leads to an inadequacy of features [28]. Given that these models undergo supervised pre-training, there is an implicit expectation of their efficacy in downstream tasks. It is essential to recognize that during pre-training, the specifics of downstream tasks are typically unknown,  
Figure 1: RCDAM-Net Network Framework. highlighting the risk of prematurely discarding potentially valuable information. Therefore, to cultivate a more generalized representation, it is advisable to retain a broader spectrum of original information rather than compressing or eliminating it. This approach could facilitate richer knowledge acquisition in pre-training, thereby enhancing performance in various downstream applications. The targets studied in this paper (bird's nests, balloons, kites, trash bags) have the characteristics of small targets and subtle features. They have a small spatial distribution in the image, which may make it difficult for the model to extract enough features to correctly identify these small targets. Taking this feature into account, we use the RevCol network to allow the model to retain a wider range of original information when extracting features. Because during pre-training, the model is unknown to subsequent downstream tasks, useful features may be discarded during the feature extraction stage. The RevCol network can enhance the acquisition of feature information in the pre-training stage, thereby improving the performance of downstream tasks. RevCol [29] structure adopts a multi-input approach, which consists of multiple reversible multilevel fusion modules. The RevCol introduced the idea of disentangled feature learning [30] into model design and proposed using reversible column as a unit to transfer information to ensure feature decoupling, while ensuring that information is not lost in network transmission. The network structure includes multiple columns, which can increase the sensory field. And reversible connections [31] are added between the columns, so that the low-level texture details and high semantic information can be separated gradually by accessing the inputs to the columns repeatedly. The specific structure is shown in Figure 2. The merit of this approach lies in its dual capacity to maintain high accuracy during pre-training while preserving essential low-level information. This balance is crucial for achieving superior results in subsequent detection tasks. This method begins by dividing the input image into several non-overlapping blocks. Each block is then processed through a distinct sub-network (column), each employing a unique ConvNeXt structure [32] with different weights. Within these sub-networks, the image blocks undergo a four-layer propagation process. Initially, a fusion unit harmonizes the dimensions of inputs across different layers. Subsequently, these unified inputs pass through a series of ConvNeXt blocks, eventually combining with the inputs of reversible operations to produce the final output. Notably, each column yields four levels of feature  
Figure 2: Reversible Column Networks Framework. maps. The feature maps closer to the input emphasize high-level semantics, whereas those at further levels focus on low-level semantics.